CN111583223A - Defect detection method, defect detection device, computer equipment and computer readable storage medium - Google Patents
Defect detection method, defect detection device, computer equipment and computer readable storage medium Download PDFInfo
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Abstract
The invention discloses a defect detection method, a defect detection device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information; analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected; analyzing the picture information by using an image depth learning algorithm to obtain a second detection result of the product to be detected; and obtaining a final detection result of the product to be detected according to the first detection result and the second detection result. The scheme provided by the invention can combine the image analysis algorithm and the image deep learning algorithm, improve the detection rate of defects, solve the dispute of judgment of good products and defective products and reduce the misjudgment rate of products to be detected.
Description
Technical Field
The embodiment of the invention relates to the technical field of product detection, in particular to a defect detection method, a defect detection device, computer equipment and a computer-readable storage medium.
Background
The surface defects refer to the defects of spots, pits, color difference, scratches, defects and the like on the surface of a product. In the industrial field, surface defects directly affect the aesthetic, performance, etc. attributes of the product, and therefore, the surface quality of the product is of great importance.
In order to ensure the surface quality of the product, the surface defects of the product need to be detected. The detection methods of surface defects are mainly classified into manual detection methods and machine detection methods. The manual detection method consumes a large amount of manpower, and manual detection often cannot guarantee that the detection standards of each person are consistent; the machine detection method is divided into two categories, namely an image analysis algorithm and an image deep learning algorithm, and the two categories of algorithms cannot avoid the occurrence of misjudgment conditions, so that the detection rate of defects is low.
Disclosure of Invention
The embodiment of the invention provides a defect detection method, a defect detection device, computer equipment and a computer readable storage medium, which can combine an image analysis algorithm and an image deep learning algorithm, improve the detection rate of defects, solve the judgment dispute of good products and defective products and reduce the misjudgment rate of products to be detected.
In an embodiment, an embodiment of the present invention provides a defect detection method, where the defect detection method includes:
acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information;
analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
Optionally, acquiring image information of a product to be detected includes:
acquiring picture information;
and preprocessing the picture information to obtain contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
Optionally, analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected, including:
dividing the contour information into a defect area and a normal area by using an image analysis algorithm;
respectively calculating the area of the defect region and the area of the normal region;
if the ratio of the area of the defect area to the area of the normal area is greater than or equal to a preset threshold value, the first detection result is a defective product;
and if the ratio of the area of the defect region to the area of the normal region is smaller than a preset threshold value, the first detection result is a good product.
Optionally, the image deep learning algorithm includes an image deep learning strict model and an image deep learning loose model, and the tolerance of the image deep learning strict model to the defect is lower than that of the image deep learning loose model to the defect.
Optionally, when the first detection result is a defective product, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected, including:
inputting picture information into an image deep learning strict model to obtain a second detection result;
correspondingly, according to the first detection result and the second detection result, obtaining a final detection result of the product to be detected, including:
if the first detection result is a defective product and the second detection result is a defective product, the final detection result is a defective product;
and if the first detection result is a defective product and the second detection result is a good product, the final detection result is a good product.
Optionally, when the first detection result is a good product, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected, including:
inputting the picture information into an image deep learning loose model to obtain a second detection result;
correspondingly, according to the first detection result and the second detection result, obtaining a final detection result of the product to be detected, including:
if the first detection result is good and the second detection result is good, the final detection result is good;
and if the first detection result is good and the second detection result is bad, the final detection result is bad.
Optionally, the defect detection method further includes:
acquiring historical image information;
and training to obtain an image deep learning strict model and an image deep learning loose model according to historical image information.
In an embodiment, an embodiment of the present invention further provides a defect detection apparatus, where the defect detection apparatus includes an image processing module, a first analysis module, a second analysis module, and a result analysis module;
the image processing module is used for acquiring image information of a product to be detected, and the image information comprises picture information and outline information;
the first analysis module is used for analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
the second analysis module is used for analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and the result analysis module is used for obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
In an embodiment, an embodiment of the present invention further provides a computer device, including: a processor for implementing the method of any of the above embodiments when executing the computer program.
In an embodiment, the embodiment of the present invention further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method of any one of the above embodiments.
In an embodiment, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the defect detection method provided in any embodiment of the present application when executing the computer program.
In an embodiment, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the defect detection method provided in any embodiment of the present application.
The embodiment of the invention provides a defect detection method, a defect detection device, computer equipment and a computer readable storage medium. According to the method, firstly, a product to be detected is detected by adopting an image analysis algorithm to obtain a first detection result; then, detecting a product to be detected by adopting an image deep learning algorithm to obtain a second detection result; and finally, comprehensively analyzing to obtain a final detection result of the product to be detected by combining the first detection result and the second detection result. The image analysis algorithm and the image deep learning algorithm are combined, and form a complementary and antagonistic relation, so that the detection rate of defects is improved, the judgment dispute of good products and defective products is solved to a certain extent, and the misjudgment rate of products to be detected is reduced.
Drawings
FIG. 1 is a schematic flow chart of a defect detection method according to an embodiment;
FIG. 2 is a flow chart illustrating another defect detection method according to an embodiment;
FIG. 3 is a diagram illustrating picture information and contour information of a product to be detected according to an embodiment;
FIG. 4 is a diagram illustrating picture information and outline information of another product to be detected according to an exemplary embodiment;
FIG. 5 is a flowchart illustrating a defect detection method according to yet another embodiment;
FIG. 6 is a schematic structural diagram of a defect detection apparatus according to an embodiment;
FIG. 7 is a schematic diagram illustrating an exemplary embodiment of a defect detection apparatus;
fig. 8 is a schematic structural diagram of a computer device according to an embodiment.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
In the field of industrial product defect detection, methods for detecting surface defects are mainly divided into manual detection methods and machine detection methods. Aiming at a machine detection method, the method is mainly divided into two categories of an image analysis algorithm and an image deep learning algorithm, and the two categories of algorithms respectively have advantages and disadvantages: the image analysis algorithm can accurately calculate the position and the area size of the defect, the judgment on the defect degree is more accurate, but the image analysis algorithm is only effective for the more obvious defect, and has poor effect on some more special and unobvious defects (such as tiny foreign matters and the like), and the defects can not be detected; the image deep learning algorithm has a high detection rate on defects, but the judgment on the defect degree (such as the defect area size) is not as simple and direct as the image analysis algorithm. Therefore, the image analysis algorithm and the image deep learning algorithm are controversial in judging good products and poor products.
Therefore, the invention provides a defect detection method, a defect detection device, computer equipment and a computer readable storage medium. Firstly, detecting a product to be detected by adopting an image analysis algorithm to obtain a first detection result; then, detecting a product to be detected by adopting an image deep learning algorithm to obtain a second detection result; and finally, comprehensively analyzing to obtain a final detection result of the product to be detected by combining the first detection result and the second detection result. The image analysis algorithm and the image deep learning algorithm are combined, and form a complementary and antagonistic relation, so that the detection rate of defects is improved, the judgment dispute of good products and defective products is solved to a certain extent, and the misjudgment rate of products to be detected is reduced.
The following embodiments of the present invention may be implemented individually, or in combination with each other, and the embodiments of the present invention are not limited in this respect. In the embodiments of the present invention, the various components are described by "first", "second", and the like, but these components should not be limited by these terms. These terms are only used to distinguish one component from another.
The following describes a defect detection method and apparatus, and technical effects thereof.
Fig. 1 is a schematic flowchart of a defect detection method according to an embodiment, and as shown in fig. 1, the method according to the embodiment is applicable to a defect detection apparatus, and includes the following steps.
S101, image information of a product to be detected is obtained, wherein the image information comprises picture information and outline information.
Specifically, the method for acquiring the image information of the product to be detected may include the following two steps:
step 1, obtaining picture information.
The picture information may be acquired by the defect detection apparatus itself, or acquired by the defect detection apparatus from other devices.
The defect detection apparatus may be any apparatus having a defect detection function, such as a terminal device, a computer, or the like. Optionally, the defect detecting apparatus may include an image capturing device (such as a camera or a video camera) for capturing picture information of the product to be detected. Of course, when the defect detection device does not include the image acquisition device, the defect detection device may be electrically connected to the image acquisition device to receive the picture information of the product to be detected, which is taken by the image acquisition device.
And 2, preprocessing the picture information to obtain contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
The step of preprocessing the picture information refers to processing the picture information by at least one processing method of image graying processing, binarization processing, expansion processing and corrosion processing to obtain the outline of the product to be detected.
The R (red) G (green) B (blue) color mode assigns an intensity value in the range of 0-255 to the RGB components of each pixel in the image using the RGB model. The RGB image uses only three colors to enable them to be mixed in different proportions, presenting 16777216(256 × 256) colors on the screen. In the RGB model, if R ═ G ═ B, the color represents a gray scale color, where the value of R ═ G ═ B is called the gray scale value, so that each pixel of the gray scale image only needs one byte to store the gray scale value (also called the intensity value, luminance value), and the gray scale range is 0-255. For example, a 0% grayscale RGB value is 255,255,255; the RGB value for 1% gray is 253,253,253; the 2% grayscale RGB value is 250,250,250.
The gray image is different from the black and white image, the black and white image only has two colors of black and white in the computer image field, and the gray image has a plurality of levels of color depth between black and white. The image graying processing provided in this embodiment may select any one of a component method (taking the brightness of three components in a color image as the grayscale values of three grayscale images, and selecting one grayscale image according to application requirements), a maximum value method (taking the maximum value of the brightness of three components in a color image as the grayscale value of a grayscale image), an average value method (averaging the brightness of three components in a color image to obtain one grayscale image), and a weighted average method (weighted averaging the brightness of three components in a color image by different weights according to importance and other indexes).
Binarization (Thresholding) is a simple method of image segmentation. The binarization process may convert the grayscale image into a binary image. The pixel gray scale larger than a certain critical gray scale value is set as a gray scale maximum value, and the pixel gray scale smaller than the value is set as a gray scale minimum value, so that binarization is realized. According to different threshold value selections, the binarization algorithm can be divided into a fixed threshold value and an adaptive threshold value. The commonly used binarization methods are: bimodal, P-parametric, iterative, and OTSU.
Both the Dilation process (Dilation) and Erosion process (Erosion) belong to morphological operations and are a series of image processing operations based on shape. Dilation and erosion processes are commonly used to remove noise, segment (isolate) individual picture elements, join (join) adjacent picture elements, find distinct areas of maxima or minima in an image.
S102, analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected.
The contour information is analyzed by using an image analysis algorithm, so that the contours and positions of some obvious defects can be obtained, and the areas of the defects can be accurately calculated. By analyzing the outline, position and area of the defect, a first detection result of the product to be detected can be obtained, namely, whether the product to be detected is a good product or a defective product is judged by utilizing an image analysis algorithm.
S103, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected.
The image deep learning algorithm comprises an image deep learning strict model and an image deep learning loose model, wherein the tolerance of the image deep learning strict model to the defects is lower than that of the image deep learning loose model to the defects. And analyzing the picture information by selecting a proper model to obtain a second detection result of the product to be detected, namely judging whether the product to be detected is a good product or a defective product by using an image deep learning algorithm.
Image deep learning rigorous model (High): the tolerance to the defects is lower and is more strict, so that the image deep learning strict model can be approximately considered as a good product if the image deep learning strict model is judged to be a good product; however, if the image deep learning strict model is determined to be defective, the image is not necessarily defective. Namely, the image deep learning strict model has high reliability of judging good products.
Image deep learning loose model (Low): the tolerance to the defects is higher and looser, so that the defects can be approximately considered as defective products if the image deep learning loose model judges the defective products; however, if the loose model for image deep learning determines that the product is good, the product is not necessarily good. Namely, the image deep learning loose model has high reliability of judging defective products.
And S104, obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
And (5) combining the detection results of the steps S102 and S103, and combining the image analysis algorithm and the image deep learning algorithm to obtain a final detection result of the product to be detected. I.e. whether the product to be tested is finally good or defective.
Fig. 2 is a schematic flow chart of another defect detection method according to an embodiment, and as shown in fig. 2, the method according to the embodiment is suitable for a defect detection apparatus, and different from the embodiment shown in fig. 1, the embodiment shown in fig. 2 explains in detail a relationship between an image analysis algorithm and an image deep learning algorithm, which are complementary and antagonistic. The method comprises the following steps.
S201, image information of a product to be detected is obtained, wherein the image information comprises picture information and outline information.
Specifically, the method for acquiring the image information of the product to be detected may include the following two steps:
step 1, obtaining picture information.
And 2, preprocessing the picture information to obtain contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
S202, dividing the contour information into a defect area and a normal area by using an image analysis algorithm.
Fig. 3 is a schematic diagram of picture information and contour information of a product to be detected according to an embodiment, as shown in fig. 3, fig. 3(a) is the picture information of the product to be detected, and fig. 3(b) is the contour information of the product to be detected in fig. 3 (a). The contour information is analyzed by using an image analysis algorithm, and can be divided into a defect area and a normal area. Referring to fig. 3(b), a white region is a normal region (may also be referred to as a good region), a black region is a background region, and a gray region is a defective region.
And S203, respectively calculating the area of the defect area and the area of the normal area.
By accurately quantifying the area of the defective region and the area of the normal region, the size and position of the defective region can be specified.
And S204, judging the relation between the ratio of the area of the defect area to the area of the normal area and a preset threshold value.
And judging the relation between the ratio of the area of the defect area to the area of the normal area and a preset threshold value, and obtaining a detection result of detecting the product to be detected by adopting an image analysis algorithm.
Namely: if the ratio of the area of the defect area to the area of the normal area is greater than or equal to a preset threshold value, the first detection result is a defective product; and if the ratio of the area of the defect region to the area of the normal region is smaller than a preset threshold value, the first detection result is a good product.
The value of the preset threshold can be set according to the requirements of actual products.
However, the image analysis algorithm has a poor detection effect on some special and unobvious defects (such as hair-like foreign matters, thin and long obvious defects, light leakage spots, and the like). Fig. 4 is a schematic diagram of picture information and outline information of another product to be detected according to an embodiment, where fig. 4(a) is the picture information (with hair-like foreign matters) of the product to be detected, and fig. 4(b) is the outline information of the product to be detected in fig. 4 (a); fig. 4(c) is picture information (with a long and thin scratch) of the product to be detected, and fig. 4(d) is profile information of the product to be detected in fig. 4 (c); fig. 4(e) is picture information (with a light leakage bright point) of a product to be detected, and fig. 4(f) is outline information of the product to be detected in fig. 4 (e). Referring to fig. 4(b), 4(d) and 4(f), the contour information of the product to be detected cannot find the defect. Therefore, it is necessary to verify the first detection result obtained by using the image analysis algorithm by using the image deep learning algorithm.
And S205, if the ratio of the area of the defect region to the area of the normal region is greater than or equal to a preset threshold, determining that the first detection result is a defective product.
And S206, inputting the picture information into the image deep learning strict model to obtain a second detection result.
And selecting an image deep learning strict model for verifying the product to be detected with the first detection result as a defective product. Specifically, the picture information is input into the image deep learning strict model to obtain a second detection result.
Image deep learning rigorous model (High): the tolerance to the defects is lower and is more strict, so that the image deep learning strict model can be approximately considered as a good product if the image deep learning strict model is judged to be a good product; however, if the image deep learning strict model is determined to be defective, the image is not necessarily defective. Namely, the image deep learning strict model has high reliability of judging good products.
And S207, if the first detection result is a defective product and the second detection result is a defective product, the final detection result is a defective product.
S208, if the first detection result is a defective product and the second detection result is a good product, the final detection result is a good product.
And S209, if the ratio of the area of the defect region to the area of the normal region is smaller than a preset threshold value, determining that the first detection result is a good product.
S210, inputting the picture information into the image deep learning loose model to obtain a second detection result.
And selecting an image deep learning loose model for verifying the product to be detected with the first detection result as a good product. Specifically, the picture information is input into the image deep learning loose model to obtain a second detection result.
Image deep learning loose model (Low): the tolerance to the defects is higher and looser, so that the defects can be approximately considered as defective products if the image deep learning loose model judges the defective products; however, if the loose model for image deep learning determines that the product is good, the product is not necessarily good. Namely, the image deep learning loose model has high reliability of judging defective products.
And S211, if the first detection result is good and the second detection result is good, the final detection result is good.
S212, if the first detection result is good and the second detection result is bad, the final detection result is bad.
The steps can be seen in that firstly, an image analysis algorithm is adopted to detect a product to be detected to obtain a first detection result; then, detecting a product to be detected by adopting an image deep learning algorithm to obtain a second detection result; and finally, comprehensively analyzing to obtain a final detection result of the product to be detected by combining the first detection result and the second detection result. The image analysis algorithm and the image deep learning algorithm are mutually assisted to fully cover the defects, so that the detection rate of the defects is improved.
However, at the same time, the image analysis algorithm and the image deep learning algorithm are "questioned" each other. The image analysis algorithm is judged to be good, and the image deep learning algorithm is required to judge the image deep learning algorithm to be defective as far as possible (namely the image analysis algorithm is judged to be good and then verified by using an image deep learning loose model, because the image deep learning loose model has high judgment reliability on defective products, the image deep learning loose model is used for detecting the defective products again, and if the image deep learning loose model judges the defective products, the image deep learning algorithm is judged to be defective products, and the judgment result is taken as the standard, and the defective products are judged); the image analysis algorithm judges the defective products, and the image deep learning algorithm judges the defective products as good as possible (namely the image analysis algorithm judges the defective products and verifies the defective products by using the image deep learning strict model, because the image deep learning strict model has high judgment reliability on the defective products, the image deep learning strict model is used for detecting the defective products again, and if the image deep learning strict model judges the defective products, the image deep learning algorithm is used for judging the defective products as standard, and the defective products are judged).
Fig. 5 is a schematic flowchart of another defect detection method according to an embodiment, and as shown in fig. 5, the method according to the embodiment is applicable to a defect detection apparatus, and unlike the embodiment shown in fig. 1, the embodiment shown in fig. 5 involves training of a model. The method comprises the following steps.
S301, acquiring historical image information.
The historical image information can be image information of a product to be detected which is detected in a historical mode, and can also be selected image information used for model training.
And S302, training to obtain an image deep learning strict model and an image deep learning loose model according to historical image information.
When a strict image deep learning model and a loose image deep learning model are trained, targeted training can be performed according to the type and the size of defects according to the strict detection requirement.
Illustratively, in training a model, the same set of training products (assumed to include product 1, product 2, and product 3) will have different corresponding training data for the image deep learning rigorous model and the image deep learning loose model. The reason is that the tolerance of the image deep learning strict model to the defects is low, and the tolerance of the image deep learning loose model to the defects is high, for example, the product 1 is a defective product under the detection of the image deep learning strict model and a good product under the detection of the image deep learning loose model; the product 2 is a defective product under the detection of a strict image deep learning model and is a defective product under the detection of a loose image deep learning model; the product 3 is a good product under the detection of a strict image deep learning model and is also a good product under the detection of a loose image deep learning model. Therefore, the training data of the strict image Deep Learning model and the training data of the loose image Deep Learning model can be selectively set and respectively input into a Deep Learning (DL) model for training, so that the strict image Deep Learning model and the loose image Deep Learning model can be obtained.
Deep Learning (DL) algorithm belongs to the field of Machine Learning (ML), and is a process of Learning intrinsic rules and representation levels of sample data, and information obtained in the Learning process is very helpful for interpretation of data such as characters, images and sounds. The deep learning algorithm can be realized by a deep learning model, and in the application, for example, a convolutional neural network (convolutional neural network) model, a Deep Belief Network (DBN) model and a stacked auto-encoder network (stacked auto-encoder network) model can be adopted to train an image deep learning strict model and an image deep learning loose model.
S303, acquiring image information of the product to be detected, wherein the image information comprises picture information and outline information.
S304, analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected.
S305, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected.
S306, obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
The embodiment of the invention provides a defect detection method, which comprises the following steps: acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information; analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected; analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected; and obtaining a final detection result of the product to be detected according to the first detection result and the second detection result. According to the method, firstly, a product to be detected is detected by adopting an image analysis algorithm to obtain a first detection result; then, detecting a product to be detected by adopting an image deep learning algorithm to obtain a second detection result; and finally, comprehensively analyzing to obtain a final detection result of the product to be detected by combining the first detection result and the second detection result. The image analysis algorithm and the image deep learning algorithm are combined, and form a complementary and antagonistic relation, so that the detection rate of defects is improved, the judgment dispute of good products and defective products is solved to a certain extent, and the misjudgment rate of products to be detected is reduced.
Fig. 6 is a schematic structural diagram of a defect detection apparatus according to an embodiment, as shown in fig. 6, the defect detection apparatus includes an image processing module 10, a first analysis module 11, a second analysis module 12, and a result analysis module 13;
the image processing module 10 is used for acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information;
the first analysis module 11 is configured to analyze the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
the second analysis module 12 is configured to analyze the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and the result analysis module 13 is configured to obtain a final detection result of the product to be detected according to the first detection result and the second detection result.
The defect detection apparatus provided in this embodiment is a defect detection method for implementing the above embodiment, and the implementation principle and technical effect of the defect detection apparatus provided in this embodiment are similar to those of the above embodiment, and are not described herein again.
Optionally, the image processing module 10 is specifically configured to obtain picture information; and preprocessing the picture information to obtain contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
Optionally, the first analysis module 11 is specifically configured to divide the contour information into a defect region and a normal region by using an image analysis algorithm; respectively calculating the area of the defect region and the area of the normal region; if the ratio of the area of the defect area to the area of the normal area is greater than or equal to a preset threshold value, the first detection result is a defective product; and if the ratio of the area of the defect region to the area of the normal region is smaller than a preset threshold value, the first detection result is a good product.
Optionally, the image deep learning algorithm includes an image deep learning strict model and an image deep learning loose model, and the tolerance of the image deep learning strict model to the defect is lower than that of the image deep learning loose model to the defect.
Optionally, when the first detection result is a defective product, the second analysis module 12 is specifically configured to input the picture information into the image deep learning strict model to obtain a second detection result;
correspondingly, the result analysis module 13 is specifically configured to determine that the final detection result is a defective product if the first detection result is a defective product and the second detection result is a defective product; and if the first detection result is a defective product and the second detection result is a good product, the final detection result is a good product.
Optionally, when the first detection result is a good product, the second analysis module 12 is specifically configured to input the picture information into the image deep learning loose model to obtain a second detection result;
correspondingly, the result analysis module 13 is specifically configured to determine that the final detection result is good if the first detection result is good and the second detection result is good; and if the first detection result is good and the second detection result is bad, the final detection result is bad.
Optionally, with reference to fig. 6, fig. 7 is a schematic structural diagram of another defect detection apparatus provided in an embodiment, as shown in fig. 7, further including: a training module 14;
a training module 14, configured to obtain historical image information; and training to obtain an image deep learning strict model and an image deep learning loose model according to historical image information.
The embodiment of the invention provides computer equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps of the defect detection method provided by any embodiment of the application:
acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information;
analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring image information of a product to be detected, comprising:
acquiring picture information;
and preprocessing the picture information to obtain contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected, wherein the method comprises the following steps:
dividing the contour information into a defect area and a normal area by using an image analysis algorithm;
respectively calculating the area of the defect region and the area of the normal region;
if the ratio of the area of the defect area to the area of the normal area is greater than or equal to a preset threshold value, the first detection result is a defective product;
and if the ratio of the area of the defect region to the area of the normal region is smaller than a preset threshold value, the first detection result is a good product.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
the image deep learning algorithm comprises an image deep learning strict model and an image deep learning loose model, wherein the tolerance of the image deep learning strict model to the defects is lower than that of the image deep learning loose model to the defects.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
when the first detection result is a defective product, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected, wherein the second detection result comprises the following steps:
inputting picture information into an image deep learning strict model to obtain a second detection result;
correspondingly, according to the first detection result and the second detection result, obtaining a final detection result of the product to be detected, including:
if the first detection result is a defective product and the second detection result is a defective product, the final detection result is a defective product;
and if the first detection result is a defective product and the second detection result is a good product, the final detection result is a good product.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
when the first detection result is a good product, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected, wherein the second detection result comprises the following steps:
inputting the picture information into an image deep learning loose model to obtain a second detection result;
correspondingly, according to the first detection result and the second detection result, obtaining a final detection result of the product to be detected, including:
if the first detection result is good and the second detection result is good, the final detection result is good;
and if the first detection result is good and the second detection result is bad, the final detection result is bad.
In an embodiment, the processor, when executing the computer program, further performs the steps of:
the defect detection method further includes:
acquiring historical image information;
and training to obtain an image deep learning strict model and an image deep learning loose model according to historical image information.
Fig. 8 is a schematic structural diagram of a computer device according to an embodiment, as shown in fig. 8, the computer device includes a processor 30, a memory 31, and a communication interface 32; the number of processors 30 in the computer device may be one or more, and one processor 30 is taken as an example in fig. 8; the processor 30, the memory 31 and the communication interface 32 in the computer device may be connected by a bus or other means, and fig. 8 illustrates the connection by the bus as an example. A bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures.
The memory 31, which is a computer-readable storage medium, may be configured to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the methods in the embodiments of the present invention. The processor 30 executes at least one functional application of the computer device and data processing by executing software programs, instructions and modules stored in the memory 31, namely, implements the steps of the defect detection method described above.
The memory 31 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 31 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 31 may include memory located remotely from processor 30, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The communication interface 32 may be configured for the reception and transmission of data.
In an embodiment, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the defect detection method provided in any embodiment of the present application when executing the computer program.
In an embodiment, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the defect detection method provided in any embodiment of the present application.
An embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method implements the steps of the audio processing method provided in any embodiment of the present application, where the method includes the following steps:
acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information;
analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
In an embodiment, the computer program when executed by the processor further performs the steps of:
acquiring image information of a product to be detected, comprising:
acquiring picture information;
and preprocessing the picture information to obtain contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
In an embodiment, the computer program when executed by the processor further performs the steps of:
analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected, wherein the method comprises the following steps:
dividing the contour information into a defect area and a normal area by using an image analysis algorithm;
respectively calculating the area of the defect region and the area of the normal region;
if the ratio of the area of the defect area to the area of the normal area is greater than or equal to a preset threshold value, the first detection result is a defective product;
and if the ratio of the area of the defect region to the area of the normal region is smaller than a preset threshold value, the first detection result is a good product.
In an embodiment, the computer program when executed by the processor further performs the steps of:
the image deep learning algorithm comprises an image deep learning strict model and an image deep learning loose model, wherein the tolerance of the image deep learning strict model to the defects is lower than that of the image deep learning loose model to the defects.
In an embodiment, the computer program when executed by the processor further performs the steps of:
when the first detection result is a defective product, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected, wherein the second detection result comprises the following steps:
inputting picture information into an image deep learning strict model to obtain a second detection result;
correspondingly, according to the first detection result and the second detection result, obtaining a final detection result of the product to be detected, including:
if the first detection result is a defective product and the second detection result is a defective product, the final detection result is a defective product;
and if the first detection result is a defective product and the second detection result is a good product, the final detection result is a good product.
In an embodiment, the computer program when executed by the processor further performs the steps of:
when the first detection result is a good product, analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected, wherein the second detection result comprises the following steps:
inputting the picture information into an image deep learning loose model to obtain a second detection result;
correspondingly, according to the first detection result and the second detection result, obtaining a final detection result of the product to be detected, including:
if the first detection result is good and the second detection result is good, the final detection result is good;
and if the first detection result is good and the second detection result is bad, the final detection result is bad.
In an embodiment, the computer program when executed by the processor further performs the steps of:
the defect detection method further includes:
acquiring historical image information;
and training to obtain an image deep learning strict model and an image deep learning loose model according to historical image information.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. Computer-readable storage media include (a non-exhaustive list): an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, Radio Frequency (RF), etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, Ruby, Go, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the internet using an internet service provider).
It will be clear to a person skilled in the art that the term user terminal covers any suitable type of wireless user equipment, such as a mobile phone, a portable data processing device, a portable web browser or a car mounted mobile station.
In general, the various embodiments of the invention may be implemented in hardware or special purpose circuits, software, logic or any combination thereof. For example, some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software which may be executed by a controller, microprocessor or other computing device, although the invention is not limited thereto.
Embodiments of the invention may be implemented by a data processor of a mobile device executing computer program instructions, for example in a processor entity, or by hardware, or by a combination of software and hardware. The computer program instructions may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages.
Any logic flow block diagrams in the figures of the present invention may represent program steps, or may represent interconnected logic circuits, modules, and functions, or may represent a combination of program steps and logic circuits, modules, and functions. The computer program may be stored on a memory. The memory may be of any type suitable to the local technical environment and may be implemented using any suitable data storage technology, such as, but not limited to, Read Only Memory (ROM), Random Access Memory (RAM), optical storage devices and systems (digital versatile disks, DVDs, or CD discs), etc. The computer readable medium may include a non-transitory storage medium. The data processor may be of any type suitable to the local technical environment, such as but not limited to general purpose computers, special purpose computers, microprocessors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Programmable logic devices (FGPAs), and processors based on a multi-core processor architecture.
Claims (10)
1. A defect detection method, comprising:
acquiring image information of a product to be detected, wherein the image information comprises picture information and outline information;
analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
2. The defect detection method of claim 1, wherein the acquiring image information of the product to be detected comprises:
acquiring the picture information;
and preprocessing the picture information to obtain the contour information, wherein the preprocessing comprises at least one of image graying processing, binarization processing, expansion processing and corrosion processing.
3. The defect detection method of claim 1, wherein the analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected comprises:
dividing the contour information into a defect area and a normal area by using an image analysis algorithm;
calculating the area of the defect region and the area of the normal region respectively;
if the ratio of the area of the defect region to the area of the normal region is greater than or equal to a preset threshold value, the first detection result is a defective product;
and if the ratio of the area of the defect region to the area of the normal region is smaller than the preset threshold value, the first detection result is a good product.
4. The defect detection method of claim 1, wherein the image deep learning algorithm comprises an image deep learning rigorous model and an image deep learning loose model, and the tolerance of the image deep learning rigorous model to the defect is lower than that of the image deep learning loose model.
5. The defect detection method of claim 4, wherein when the first detection result is a defective product, the analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected comprises:
inputting the picture information into the strict image deep learning model to obtain the second detection result;
correspondingly, the obtaining of the final detection result of the product to be detected according to the first detection result and the second detection result includes:
if the first detection result is a defective product and the second detection result is a defective product, the final detection result is a defective product;
and if the first detection result is a defective product and the second detection result is a good product, the final detection result is a good product.
6. The defect detection method of claim 4, wherein when the first detection result is a good product, the analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected comprises:
inputting the picture information into the image deep learning loose model to obtain the second detection result;
correspondingly, the obtaining of the final detection result of the product to be detected according to the first detection result and the second detection result includes:
if the first detection result is a good product and the second detection result is a good product, the final detection result is a good product;
and if the first detection result is a good product and the second detection result is a defective product, the final detection result is a defective product.
7. The defect detection method of claim 4, further comprising:
acquiring historical image information;
and training to obtain the strict image deep learning model and the loose image deep learning model according to the historical image information.
8. A defect detection device is characterized by comprising an image processing module, a first analysis module, a second analysis module and a result analysis module;
the image processing module is used for acquiring image information of a product to be detected, and the image information comprises picture information and outline information;
the first analysis module is used for analyzing the contour information by using an image analysis algorithm to obtain a first detection result of the product to be detected;
the second analysis module is used for analyzing the picture information by using an image deep learning algorithm to obtain a second detection result of the product to be detected;
and the result analysis module is used for obtaining a final detection result of the product to be detected according to the first detection result and the second detection result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the defect detection method of any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the defect detection method according to any one of claims 1 to 7.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112505049A (en) * | 2020-10-14 | 2021-03-16 | 上海互觉科技有限公司 | Mask inhibition-based method and system for detecting surface defects of precision components |
| CN112611757A (en) * | 2020-12-23 | 2021-04-06 | 深兰人工智能芯片研究院（江苏）有限公司 | AI auxiliary detection method, device, electronic equipment and computer readable storage medium |
| CN112669271A (en) * | 2020-12-22 | 2021-04-16 | 深圳酷派技术有限公司 | Object surface defect detection method, related device and computer storage medium |
| CN112785556A (en) * | 2020-12-31 | 2021-05-11 | 深兰人工智能芯片研究院（江苏）有限公司 | Reinspection method, reinspection device, electronic equipment and computer-readable storage medium |
| CN113066079A (en) * | 2021-04-19 | 2021-07-02 | 北京滴普科技有限公司 | Method, system and storage medium for automatically detecting wood defects |
| CN113379728A (en) * | 2021-07-02 | 2021-09-10 | 上海电气集团股份有限公司 | Method, system, equipment and readable storage medium for detecting defects on surface of rail |
| CN113706465A (en) * | 2021-07-22 | 2021-11-26 | 杭州深想科技有限公司 | Pen defect detection method based on deep learning, computing equipment and storage medium |
| CN114782451A (en) * | 2022-06-23 | 2022-07-22 | 季华实验室 | Workpiece defect detection method and device, electronic equipment and readable storage medium |
| CN114841915A (en) * | 2022-03-14 | 2022-08-02 | 阿里巴巴（中国）有限公司 | Tile flaw detection method and system based on artificial intelligence and storage medium |
| CN114998192A (en) * | 2022-04-19 | 2022-09-02 | 深圳格芯集成电路装备有限公司 | Defect detection method, device and equipment based on deep learning and storage medium |
| WO2023226706A1 (en) * | 2022-05-25 | 2023-11-30 | 京东方科技集团股份有限公司 | Defect detection method and apparatus |
| CN118408607A (en) * | 2024-07-03 | 2024-07-30 | 南昌佛吉亚排气控制技术有限公司 | Automobile part detection method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180293721A1 (en) * | 2017-04-07 | 2018-10-11 | Kla-Tencor Corporation | Contour based defect detection |
| WO2019233166A1 (en) * | 2018-06-04 | 2019-12-12 | 杭州海康威视数字技术股份有限公司 | Surface defect detection method and apparatus, and electronic device |
| CN110992329A (en) * | 2019-11-28 | 2020-04-10 | 上海微创医疗器械（集团）有限公司 | Product surface defect detection method, electronic device and readable storage medium |
| CN111044522A (en) * | 2019-12-14 | 2020-04-21 | 中国科学院深圳先进技术研究院 | Defect detection method and device and terminal equipment |
-
2020
- 2020-05-07 CN CN202010377497.4A patent/CN111583223B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180293721A1 (en) * | 2017-04-07 | 2018-10-11 | Kla-Tencor Corporation | Contour based defect detection |
| WO2019233166A1 (en) * | 2018-06-04 | 2019-12-12 | 杭州海康威视数字技术股份有限公司 | Surface defect detection method and apparatus, and electronic device |
| CN110992329A (en) * | 2019-11-28 | 2020-04-10 | 上海微创医疗器械（集团）有限公司 | Product surface defect detection method, electronic device and readable storage medium |
| CN111044522A (en) * | 2019-12-14 | 2020-04-21 | 中国科学院深圳先进技术研究院 | Defect detection method and device and terminal equipment |
Non-Patent Citations (1)
| Title |
|---|
| 苗永菲;游洋;李赵松;黎红军;宋康;侯朝云;: "基于机器视觉的玻璃缺陷检测技术" * |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112505049A (en) * | 2020-10-14 | 2021-03-16 | 上海互觉科技有限公司 | Mask inhibition-based method and system for detecting surface defects of precision components |
| CN112505049B (en) * | 2020-10-14 | 2021-08-03 | 上海互觉科技有限公司 | Mask inhibition-based method and system for detecting surface defects of precision components |
| CN112669271A (en) * | 2020-12-22 | 2021-04-16 | 深圳酷派技术有限公司 | Object surface defect detection method, related device and computer storage medium |
| CN112611757A (en) * | 2020-12-23 | 2021-04-06 | 深兰人工智能芯片研究院（江苏）有限公司 | AI auxiliary detection method, device, electronic equipment and computer readable storage medium |
| CN112785556A (en) * | 2020-12-31 | 2021-05-11 | 深兰人工智能芯片研究院（江苏）有限公司 | Reinspection method, reinspection device, electronic equipment and computer-readable storage medium |
| CN113066079A (en) * | 2021-04-19 | 2021-07-02 | 北京滴普科技有限公司 | Method, system and storage medium for automatically detecting wood defects |
| CN113379728A (en) * | 2021-07-02 | 2021-09-10 | 上海电气集团股份有限公司 | Method, system, equipment and readable storage medium for detecting defects on surface of rail |
| CN113706465A (en) * | 2021-07-22 | 2021-11-26 | 杭州深想科技有限公司 | Pen defect detection method based on deep learning, computing equipment and storage medium |
| CN113706465B (en) * | 2021-07-22 | 2022-11-15 | 杭州深想科技有限公司 | Pen defect detection method based on deep learning, computing equipment and storage medium |
| CN114841915A (en) * | 2022-03-14 | 2022-08-02 | 阿里巴巴（中国）有限公司 | Tile flaw detection method and system based on artificial intelligence and storage medium |
| CN114998192A (en) * | 2022-04-19 | 2022-09-02 | 深圳格芯集成电路装备有限公司 | Defect detection method, device and equipment based on deep learning and storage medium |
| WO2023226706A1 (en) * | 2022-05-25 | 2023-11-30 | 京东方科技集团股份有限公司 | Defect detection method and apparatus |
| CN114782451A (en) * | 2022-06-23 | 2022-07-22 | 季华实验室 | Workpiece defect detection method and device, electronic equipment and readable storage medium |
| CN118408607A (en) * | 2024-07-03 | 2024-07-30 | 南昌佛吉亚排气控制技术有限公司 | Automobile part detection method and device |
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