CN114441554B - Detection method - Google Patents
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- CN114441554B CN114441554B CN202011229886.9A CN202011229886A CN114441554B CN 114441554 B CN114441554 B CN 114441554B CN 202011229886 A CN202011229886 A CN 202011229886A CN 114441554 B CN114441554 B CN 114441554B
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- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 28
- 238000012216 screening Methods 0.000 claims abstract description 3
- 238000000034 method Methods 0.000 claims description 16
- 238000007689 inspection Methods 0.000 claims 1
- 238000013461 design Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/956—Inspecting patterns on the surface of objects
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
- G01N2021/8861—Determining coordinates of flaws
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
- G01N21/956—Inspecting patterns on the surface of objects
- G01N2021/95638—Inspecting patterns on the surface of objects for PCB's
- G01N2021/95661—Inspecting patterns on the surface of objects for PCB's for leads, e.g. position, curvature
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- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
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- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
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- Image Analysis (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The application provides a detection method, which comprises the following steps: performing binarization processing on the input image related to the printed circuit board; judging a plurality of contour lines belonging to black-white boundaries or image frames in the input image, and defining the contour lines as a plurality of line segment objects; screening each line segment object according to the perimeter threshold value and the times of passing through the image frame to obtain a plurality of candidate line breaking objects; and judging whether the broken line object belongs to a first group of broken lines or not according to the midpoint coordinates of the broken line ends of each candidate broken line object and the first set line distance. Thereby, a detection method capable of detecting a line disconnection position of a printed circuit board without using a standard template and without requiring a large amount of data is realized.
Description
Technical Field
The present application relates to a detection method, and more particularly, to a detection method for detecting a circuit breaking position of a printed circuit board.
Background
Machine vision techniques have been widely used in the manufacturing process of printed circuit boards to detect the presence of flaws in the semifinished product. The existing method for detecting the defects of the printed circuit board mainly adopts the following two technologies: one is a standard template comparison circuit which needs perfect defect-free, namely, a data file of a circuit Layout (Layout) needs to be prepared in advance for comparison reference; and the other is to judge by machine learning through an image database with a large amount of image data under the condition that the circuit related data can not be obtained or the information can be obtained only through the image of the prior circuit board. However, whether there are other better detection methods becomes a problem to be solved.
Disclosure of Invention
The application aims to provide a detection method of an image database, which does not need to use standard templates for comparison and does not need a large amount of data.
Accordingly, the present application provides a detection method suitable for use in and carried out by a detection system, the detection method comprising steps (a) to (D).
In step (A), the input image related to the printed circuit board is subjected to binarization processing, wherein the input image is provided with an image frame.
In step (B), a plurality of contour lines belonging to a black-white boundary or the image frame are determined for the input image after the binarization processing, and are defined as a plurality of corresponding line segment objects.
In step (C), screening each line segment object according to a perimeter threshold and the number of times of passing through the image frame, so as to obtain a plurality of candidate line breaking objects.
In step (D), it is determined whether the broken line object belongs to the first group of broken lines according to the midpoint coordinates of the broken line ends of each of the candidate broken line objects and the first set line distance.
In some embodiments, in step (C), when any one of the line segment objects has a perimeter greater than or equal to the perimeter threshold and the number of passes of the line segment object through the image frame is equal to 1, the line segment object is determined as one of the candidate line segment objects.
In some embodiments, in the step (D), each of the candidate broken line objects has a design line width corresponding to a design value, and a plurality of actual line widths measured at different positions, when one of the actual line widths is equal to the design line width multiplied by a ratio threshold, a position of a center point of two end coordinates corresponding to the actual line width on the contour line is equal to the middle point coordinate of the broken line end, and the ratio threshold is greater than zero and less than 1.
In some embodiments, in step (D), a midpoint distance between the midpoint coordinate of the broken end of each of the candidate broken line objects and the midpoint coordinates of the broken end of any one of the remaining candidate broken line objects is calculated, and when it is determined that the smallest one of the at least one midpoint distances is smaller than the first set line distance, the corresponding two candidate broken line objects are classified as one pair of data of the first group.
In some embodiments, the detection method further comprises step (E) of determining whether the candidate broken line object not classified as the first group is classified as belonging to a second group according to the at least one midpoint distance of the midpoint coordinates of the broken line ends of the candidate broken line object not classified as the first group and a second set line distance, the second set line distance being greater than the first set line distance.
In some embodiments, in step (a), the image frame is quadrilateral. In step (E), when it is determined that any one of the at least one midpoint distance is smaller than the second set line distance and the image frames through which the two candidate broken line objects corresponding to the one pass are different sides of the quadrilateral, the corresponding two candidate broken line objects are classified as one pair of data of the second group.
In some aspects, the detection method further comprises step (F) of determining that the at least one candidate broken line object that is not classified as the first group or the second group is classified as belonging to a third group.
In some embodiments, in the step (D), the ratio threshold is equal to 85%, the first set pitch is equal to a minimum pitch of a process of the printed circuit board corresponding to the input image, and the second set pitch is equal to 2 times the minimum pitch of the process of the printed circuit board corresponding to the input image.
In some embodiments, in step (D), when it is determined that the smallest one of the at least one midpoint distance is equal to the first set line distance, the corresponding two candidate broken line objects are classified as one pair of data of the first group.
In other embodiments, in step (E), when it is determined that any one of the at least one midpoint distance is equal to the second set line distance and the image frames through which the two candidate broken line objects of the one pass respectively are different sides of the quadrilateral, the corresponding two candidate broken line objects are classified into one pair of data of the second group.
The application has the beneficial effects that: the processing module is used for preprocessing the input image of the printed circuit board and identifying the contour line, and classifying the candidate broken line objects according to the calculated midpoint distance of the midpoint coordinates to obtain the first group belonging to broken lines, so that the detection method of the image database without using standard templates for comparison and a large amount of data can be realized.
Drawings
FIG. 1 is a block diagram illustrating a detection system to which the detection method of the present application is applied;
FIG. 2 is a flow chart illustrating one embodiment of the detection method of the present application;
FIG. 3 is a schematic diagram illustrating an aspect of an input image of the embodiment;
FIG. 4 is a schematic diagram, assisting FIG. 3 in illustrating a partial enlarged view of the input image; and
Fig. 5 is a schematic diagram illustrating another aspect of the input image of the embodiment.
Detailed Description
The application is described in detail below with reference to the attached drawings and examples:
before the present application is described in detail, it should be noted that in the following description, like elements are denoted by the same reference numerals.
Referring to fig. 1, an embodiment of the detection method of the present application is suitable for a detection system 100, where the detection system 100 includes an image capturing module 1, a processing module 3, and a storage module 2. The image capturing module 1 is, for example, a camera or a camera device, and is disposed on a inspecting line of a printed circuit board for capturing an image including the printed circuit board. The processing module 3 is, for example, at least one cpu of a computer system, and is configured to implement a known or existing image recognition technology, and is electrically connected to the image capturing module 1 to control the image capturing module 1 to capture the image, and is also electrically connected to the storage module 2 to store the image and various data generated by the processing module 3. The storage module 2 is, for example, a disk or other storage device.
Referring to fig. 1 and 2, the detection method includes steps S1 to S13.
In step S1, the processing module 3 controls the image capturing module 1 to capture the image including the printed circuit board. Next, step S2 is performed.
In step S2, the processing module 3 takes the image as an input image, and performs binarization processing on the input image. For example, the input image is a color image, and the binarization process converts the color image into a black-and-white image, wherein each Pixel (Pixel) in the black-and-white image corresponds to a value of 0 or 1. In this embodiment, the size of the input image is 240 pixels by 240 pixels, and the input image has an image frame, for example, a square. Next, step S3 is performed. In other embodiments, the input image may be of other sizes.
In addition, the following are to be specified: in other embodiments, the image frame may be other quadrangles, or other shapes. In step S2, the processing module 3 may divide the image into a plurality of input images. Furthermore, the input images may be stored in the storage module 2 in advance, and the steps S1 and S2 may be omitted.
In step S3, the processing module 3 determines a plurality of contour lines belonging to the black-white boundary or the image frame for the binarized input image, and defines a plurality of corresponding line segment objects. Referring to fig. 3 again, fig. 3 illustrates an aspect of the input image, wherein the contours of the six line segment objects 41-46 correspond to the six contours. Next, step S4 is performed.
In step S4, the processing module 3 determines whether a circumference of each line segment object is greater than or equal to a circumference threshold. When the circumference is determined to be smaller than the circumference threshold, step S5 is performed. And when it is determined that the circumference is greater than or equal to the circumference threshold, step S6 is performed. In this embodiment, the circumference threshold is, for example, 10% of the circumference, that is 240×4×10% =96. In other embodiments, step S4 may also be to determine whether the circumference is greater than the circumference threshold.
In step S5, the line segment object is too small to be determined as a line segment of the printed circuit board, but may be bubbles, dirt, or foreign points. Referring again to FIG. 3, for example, the line segment object 43.
In step S6, the processing module 3 determines whether the number of times of each line segment object passing through the image frame is equal to 1, so as to screen the line segment objects with the corresponding number of times equal to 1 as a plurality of candidate line breaking objects respectively. When it is judged not to be equal to 1, step S7 is performed. And when it is judged to be equal to 1, step S8 is performed. Referring again to fig. 3, a plurality of line segment objects 41, 42, 45, 46 are provided as a plurality of candidate line segment objects.
In step S7, the segment object is too large or belongs to a separate object. Referring to fig. 3 again, the segment object 44 corresponds to a number of times equal to 0, belonging to the independent object.
In step S8, referring to fig. 4 again, each of the candidate broken line objects has a design line width corresponding to a design value, i.e. a theoretical width at the time of design. The processing module 3 calculates a plurality of actual line widths of each of the candidate line breaking objects, i.e. a plurality of actual line widths measured at different positions, such as three distances 463-465 in fig. 4, i.e. the three actual line widths. When the processing module 3 determines that one of the actual line widths is equal to the design line width multiplied by a ratio threshold, the position of the center point of the two end point coordinates corresponding to the actual line width on the contour line is equal to the midpoint coordinate of the line breaking end, and the ratio threshold is greater than zero and less than 1. For example, the ratio threshold is equal to 85%, the actual line width (equal to the distance 465) is equal to 85% of the designed line width, and the center point of the two end coordinates 461 and 462 (or 451 and 452, or 421 and 422, or 411 and 412) is located at a position equal to the midpoint coordinate of the broken line end of the line segment object 46 (or 45, or 42, or 41). The processing module 3 also calculates a midpoint distance between the midpoint coordinate of the wire break end of each of the candidate wire break objects and the midpoint coordinates of the wire break ends of any of the remaining candidate wire break objects. Next, step S9 is performed.
In step S9, the processing module 3 determines whether each of the candidate broken objects is a first group. When the processing module 3 determines, for each of the candidate broken line objects, that the minimum one of the at least one midpoint distance from any one of the remaining candidate broken line objects is smaller than a first set line distance, the two corresponding candidate broken line objects are classified as one pair of data belonging to the first group of broken lines, and then step S10 is performed. In contrast, the candidate broken line objects that are not classified into the first group are executed in step S11. In this embodiment, the first set line distance is equal to a minimum line distance of the printed circuit board corresponding to the input image, and in other embodiments, the first set line distance may be, for example, but not limited to, a minimum value of the design line distance of the line segment object in the input image.
In step S10, the data belonging to the first group is outputted through the processing module 3. The data is, for example, the midpoint coordinates of the line breaking ends of one or more pairs of two candidate line breaking objects, or (and) the endpoint coordinates corresponding to the midpoint coordinates.
In step S11, the processing module 3 determines whether the candidate broken objects that are not classified into the first group are a second group, where the second group is classification data indicating that a broken line may exist. When the processing module 3 determines that any one of the at least one midpoint distance is smaller than a second set line distance for each of the candidate broken line objects, and the image frames through which the two candidate broken line objects corresponding to the midpoint distance respectively pass are different sides of the quadrilateral, the corresponding two candidate broken line objects are classified into one pair of data of the second group, and then step S12 is performed. Conversely, the candidate broken line objects that are not classified as the second group are executed in step S13. In the present embodiment, the second set line distance is equal to, but not limited to, 2 times the minimum line distance of the printed circuit board corresponding to the input image.
In step S12, the data belonging to the second group is outputted through the processing module 3. The data is, for example, the midpoint coordinates of the line breaking ends of one or more pairs of two candidate line breaking objects, or (and) the endpoint coordinates corresponding to the midpoint coordinates.
In step S13, the processing module 3 determines that the candidate broken line object that is not classified into the second group is a third group, and outputs data belonging to the third group. The data is, for example, the at least one midpoint coordinate of the at least one wire break end of the at least one candidate wire break object or (and) the endpoint coordinate corresponding to the at least one midpoint coordinate.
Referring to fig. 5 again, fig. 5 illustrates an aspect of the input image, in which six midpoint coordinates 51-56 are defined to correspond to six candidate broken line objects a-F, respectively, and the midpoint distances are a to B (equal to B to a, and omitted below) =2.0, a to c=3.7, a to d=4.1, a to e=1.8, a to f=0.6, B to c=1.7, B to d=3.0, B to e=1.1, B to f=2.3, C to d=2.2, C to e=2.0, C to f=3.8, D to e=2.3, D to f=3.8, E to f=1.8, respectively, and the midpoint distances are the unit of the minimum line distance of the process of the printed circuit board, for example. Candidate broken line objects a and F are classified as being paired into the first group, candidate broken line objects B and E are classified as being paired into the second group, and candidate broken line objects C and D are classified as being the third group.
In addition, the following are to be specified: in steps S9 and S11, when the processing module 3 determines, for each of the candidate broken line objects, that the minimum one of the at least one midpoint distance from any one of the remaining candidate broken line objects is equal to the first set line distance, the two corresponding candidate broken line objects may be classified into the first group (or the second group) according to a predetermined rule. Similarly, in steps S11 and S13, when the processing module 3 determines, for each of the candidate broken line objects, that the minimum one of the at least one midpoint distance from any of the remaining candidate broken line objects is equal to the second set line distance, the corresponding two candidate broken line objects may be classified into the second group (or the third group) according to a predetermined rule.
In summary, the processing module 3 performs preprocessing and contour line identification on the input image of the printed circuit board, and classifies the candidate broken line objects according to the calculated midpoint distance of the midpoint coordinate, so as to obtain the first group belonging to broken lines, the second group belonging to the second group possibly broken lines, and the third group belonging to the rest.
The above description is only of the preferred embodiments of the present application, but not limited thereto, and any person skilled in the art can make further modifications and variations without departing from the spirit and scope of the present application, and the scope of the present application is defined by the appended claims.
Claims (8)
1. A method of detection suitable for use in and implemented by a detection system, the method comprising:
a: performing binarization processing on an input image related to the printed circuit board, wherein the input image is provided with an image frame;
b: for the binarized input image, judging a plurality of contour lines belonging to a black-white boundary or the image frame, and defining the contour lines as a plurality of corresponding line segment objects;
c: screening each line segment object according to a perimeter threshold value and the times of passing through the image frame to obtain a plurality of candidate line segment objects; and
D: judging whether each candidate broken line object is classified as belonging to a first group of broken lines according to the midpoint coordinates of the broken line ends of each candidate broken line object and a first set line distance, judging whether each candidate broken line object has a designed line width corresponding to a designed value and a plurality of actual line widths measured at different positions, when one of the actual line widths is equal to the designed line width multiplied by a proportion threshold value, the position of a central point of two end coordinates corresponding to the actual line width on the contour line is equal to the midpoint coordinates of the broken line ends, the proportion threshold value is larger than zero and smaller than 1, calculating the midpoint distances between the midpoint coordinates of the broken line ends of each candidate broken line object and the midpoint coordinates of the broken line ends of any one of the candidate broken line objects, and when the smallest one of the at least one midpoint distances is judged to be smaller than the first set line distance, the corresponding two candidate broken line objects are classified as one pair of the first set line distances, and the corresponding data of the first set line distances of the first set image plates are equal to the minimum image distance of the first set image plates.
2. The detecting method according to claim 1, wherein in step C, when any one of the line segment objects has a perimeter greater than or equal to the perimeter threshold and the number of times the line segment object passes through the image frame is equal to 1, the line segment object is determined as one of the candidate line segment objects.
3. The detecting method according to claim 2, further comprising a step E of determining whether the candidate broken line object not classified as the first group is classified as belonging to a second group based on the at least one midpoint distance of the midpoint coordinates of the broken line end of the candidate broken line object not classified as the first group and a second set line distance, the second set line distance being larger than the first set line distance.
4. The detecting method as claimed in claim 3, wherein in the step A, the image frame is a quadrangle, and in the step E, when it is determined that any one of the at least one midpoint distance is smaller than the second set line distance and the image frames through which the two candidate broken line objects corresponding to the midpoint distance respectively pass are different sides of the quadrangle, the corresponding two candidate broken line objects are classified into one pair of data of the second group.
5. The method of claim 4, further comprising step F of determining that at least one candidate broken line object that is not classified as either the first group or the second group is classified as belonging to a third group.
6. The inspection method of claim 5, wherein in step D, the ratio threshold is equal to 85%, and the second set line spacing is equal to 2 times the minimum line spacing of the printed circuit board process corresponding to the input image.
7. The method of claim 6, wherein in step D, when it is determined that the smallest one of the at least one midpoint distance is equal to the first set line distance, the corresponding two candidate broken line objects are classified as one pair of data of the first group.
8. The detecting method as claimed in claim 6, wherein in the step E, when it is determined that any one of the at least one midpoint distance is equal to the second set line distance and the image frames through which the two candidate broken line objects corresponding to the midpoint distance respectively pass are different sides of the quadrangle, the corresponding two candidate broken line objects are classified as one pair of data of the second group.
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