CN111798507A - Power transmission line safety distance measuring method, computer equipment and storage medium - Google Patents
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Abstract
The invention provides a method for measuring the safe distance of a power transmission line, which comprises the following steps: calibrating a binocular stereo camera; acquiring images of different positions of a target power transmission line by using a calibrated binocular stereo camera, wherein the images comprise a left eye image and a right eye image; preprocessing the image; inputting the preprocessed image into a convolutional neural network for image feature extraction and classification; performing feature matching on the extracted and classified image features by adopting a stereo matching algorithm to match the same target in the image to obtain a disparity map and a depth map; and inputting the disparity map and the depth map into a weighted least square filter, filtering out irrelevant parameters, and then performing distance measurement on the disparity map and the depth map subjected to filtering processing by using the disparity formed by the left camera and the right camera to obtain a safe distance measurement result of the power transmission line. The method has the characteristics of high efficiency, simple algorithm design, high precision and the like, and can realize high-precision calculation of the safe distance measurement of the power transmission line.
Description
Technical Field
The invention relates to the technical field of stereoscopic vision distance measurement, in particular to a method for measuring the safe distance of a power transmission line, computer equipment and a storage medium.
Background
The power transmission line fault detection is an important means for technically improving the power supply reliability of a power system and promoting the safe and stable operation of a power grid. In the conventional power line safety distance measuring method, a control signal (laser beam, infrared ray, ultrasonic wave, radio wave, etc.) is mainly transmitted to a target, and a signal transmitted through a range finder is reflected by a measured object and then received by the range finder, and the range finder simultaneously records the round trip time of the laser and is used for distance measurement. However, this method has three major disadvantages: the range of the general sensor is 1-4m, so that the precision is low; if the object is too close, it may confuse the previous pulse with the subsequent echo; various ranging methods are not universal and cannot play a role in some severe or special working environments.
Patent publication No. CN109520480A discloses a distance measurement method and a distance measurement system based on binocular stereo vision, which provide that matching cost calculation is performed on a correction reference image and a correction target image to obtain matching cost between each pixel point in the correction reference image and a corresponding point in the correction target image, then cost aggregation is performed on the matching cost to obtain corresponding accumulated cost, and then a disparity value corresponding to each pixel point in the correction reference image is calculated according to the accumulated cost to complete distance measurement. However, in the method, the matching cost calculation is directly performed on the correction reference image and the correction target image to realize the ranging function, and the noise in the image can affect the accuracy of the ranging result.
Disclosure of Invention
The invention provides a method for measuring the safe distance of a power transmission line, aiming at overcoming the defect of low distance measurement precision in the prior art.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for measuring the safe distance of a power transmission line comprises the following steps:
s1: calibrating a binocular stereo camera;
s2: acquiring images of different positions of a target power transmission line by using a calibrated binocular stereo camera, wherein the images comprise a left eye image and a right eye image;
s3: preprocessing the image;
s4: inputting the preprocessed image into a convolutional neural network for image feature extraction and classification;
s5: performing feature matching on the extracted and classified image features by adopting a stereo matching algorithm to match the same target in the image to obtain a disparity map and a depth map;
s6: and inputting the disparity map and the depth map into a weighted least square filter, filtering out irrelevant parameters, and then performing distance measurement on the disparity map and the depth map subjected to filtering processing by using the disparity formed by the left camera and the right camera to obtain a safe distance measurement result of the power transmission line.
In the technical scheme, the binocular stereo camera is calibrated for eliminating image distortion, so that the acquired image information is more accurate, the acquired image is preprocessed and then input into a convolutional neural network for feature extraction and classification of a target, a stereo matching algorithm is adopted for multi-angle recognition and matching of the image features of the target, a weighted least square filter is adopted for enabling the output image and the input image to be similar as far as possible after smoothing, the original state can be kept at the edge part as far as possible, and finally the distance between the transmission line and the camera is measured safely in the environment of a binocular stereo system through mathematical calculation.
Preferably, in the step S1, calibrating the binocular stereo camera includes camera internal reference calibration and external reference calibration, where the internal reference calibration adopts a Zhang Zhengyou calibration method, and the external reference calibration obtains a translation matrix T and a rotation matrix R of the right camera relative to the left camera.
Preferably, in the step S3, the specific step of preprocessing the image includes: binocular image calibration, distortion correction and three-dimensional correction.
Preferably, the binocular image calibration comprises the following specific steps: by solving the corresponding relation between the coordinate points in the three-dimensional space and the two-dimensional image coordinate points of the image, the transformation matrix is solved by utilizing the two-dimensional image coordinates and the physical coordinates of the three-dimensional space acquired by the chessboard board, and then the epipolar lines of the left eye image and the right eye image are positioned on the same horizontal line through the transformation matrix, namely the binocular image calibration is completed.
Preferably, in step S4, the convolutional neural network includes a convolutional layer, a batch normalization layer, an average pooling layer, an activation layer, a residual layer, an identity mapping layer, a maximum pooling layer, a full-link layer, and a Softmax classifier, which are connected in sequence.
Preferably, in the step S5, the step of performing feature matching by using a stereo matching algorithm includes matching cost calculation, cost aggregation, disparity calculation, and disparity optimization.
Preferably, in step S5, the stereo matching algorithm used includes a region matching algorithm, a feature matching algorithm, a local matching algorithm, and a global matching algorithm.
Preferably, the filtering algorithm used in the weighted least square filter in the step S6 includes one of a weighted least square filtering algorithm, a bilateral filtering algorithm, and a guided filtering algorithm.
The invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores computer programs, and the processor realizes the steps of the power transmission line safe distance measuring method when executing the computer programs.
The invention also proposes 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 above-mentioned transmission line safety distance measuring method.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the method has the advantages that the images are input into the convolutional neural network for recognition after noise reduction, distance measurement calculation is performed after the images are subjected to the stereo matching algorithm, noise in the images can be effectively reduced, important information in the images is kept, the method has the advantages of being high in efficiency, simple in algorithm design, high in precision and the like, and high-precision calculation of safe distance measurement of the power transmission line of the binocular stereo system can be achieved.
Drawings
Fig. 1 is a flow chart of a transmission line safety distance measuring method.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The present embodiment provides a method for measuring a safe distance of a power transmission line, which is a flowchart of the method for measuring a safe distance of a power transmission line of the present embodiment, as shown in fig. 1.
The method for measuring the safe distance of the power transmission line provided by the embodiment comprises the following steps:
s1: and calibrating the binocular stereo camera for eliminating distortion and obtaining an internal reference distortion coefficient and an external reference matrix.
In the step, calibrating the binocular stereo camera comprises calibrating internal reference and external reference of the camera, wherein the calibration of the internal reference adopts a Zhang Zhengyou calibration method to obtain an internal reference distortion coefficient; and obtaining a translation matrix T and a rotation matrix R of the right camera relative to the left camera by external reference calibration.
In this embodiment, the internal reference calibration is used to determine the projection relationship between the camera coordinate system and the image coordinate system, and the external reference calibration is used to measure the relative position between two cameras in the binocular stereo camera.
S2: and acquiring images of different positions of the target power transmission line by using the calibrated binocular stereo camera, wherein the images comprise a left eye image and a right eye image.
S3: and preprocessing the image.
In this step, the specific steps of preprocessing the image include: binocular image calibration, distortion correction and three-dimensional correction. The binocular image calibration method specifically comprises the following steps: solving a corresponding relation between a coordinate point in a three-dimensional space and a two-dimensional image coordinate point of an image, solving a transformation matrix of the two-dimensional image coordinate and the physical coordinate of the three-dimensional space obtained by a chessboard board, and then enabling epipolar lines of a left eye image and a right eye image to be on the same horizontal line through the transformation matrix, namely completing binocular image calibration; distortion correction is carried out by adopting an internal reference distortion coefficient obtained by calibrating the internal reference in the step S1; and (4) performing epipolar line correction by adopting the rotation matrix R and the translation matrix T obtained by external parameter calibration in the step S1.
In this embodiment, the binocular image calibration is to strictly correspond the left eye image and the right eye image after distortion removal, so that epipolar lines of the two images are exactly on the same horizontal line, and thus any pixel point on one image and the corresponding point on the other image necessarily have the same line number, and only one-dimensional search needs to be performed on the line during matching.
S4: and inputting the preprocessed image into a convolutional neural network for image feature extraction and classification.
In this step, the convolutional neural network includes a convolutional layer, a batch normalization layer, an average pooling layer, an activation layer, a residual layer, an identity mapping layer, a maximum pooling layer, a full connection layer, and a Softmax classifier, which are connected in sequence.
In this embodiment, a ResNet-50 residual network structure is adopted, 1 × 1 convolutional layer dimensionality reduction is used, then 1 × 1 convolutional layer dimensionality reduction is passed, and the residual layer output is matched with the channel of the identity mapping layer and the number of channels of the identity mapping layer and the residual layer is matched.
S5: and performing feature matching on the extracted and classified image features by adopting a stereo matching algorithm to match the same target in the image to obtain a disparity map and a depth map.
In the step, the step of performing feature matching by using a stereo matching algorithm comprises matching cost calculation, cost aggregation, parallax calculation and parallax optimization. The adopted stereo matching algorithm comprises a region matching algorithm, a feature matching algorithm, a local matching algorithm and a global matching algorithm.
The stereo matching algorithm used in this embodiment mainly implements target detection by applying a target detection method to an image captured by a single camera, and then matches the detected target with the same target in an image captured by another camera, that is, a cross-correlation technique is implemented between the target detected in the image captured by the first camera and the same horizontal position in the image captured by the second camera to implement matching. Since the cross-correlation function varies between +1 and-1, it is determined that the best match is detected when the result of performing the cross-correlation technique takes a maximum value greater than a preset threshold, i.e., when the cross-correlation function takes a value close to + 1.
S6: and inputting the disparity map and the depth map into a weighted least square filter, filtering out irrelevant parameters, and then performing distance measurement on the disparity map and the depth map subjected to filtering processing by using the disparity formed by the left camera and the right camera to obtain a safe distance measurement result of the power transmission line.
In this embodiment, the weighted least square filter is used to filter the input image and the output image to make them as similar and smooth as possible, and to keep the image edge portions as original as possible.
The method for measuring the safe distance of the power transmission line provided by the embodiment solves the problem that the existing binocular stereo system is inaccurate in measuring the safe distance and the size of the power transmission line, uses an end-to-end convolution neural network ResNet-50 model to extract the characteristics of the target of the image, uses an image stereo matching technology to identify and match the target in the image in multiple angles, uses a weighted least square filtering algorithm to enable the image to be similar to the greatest extent after smoothing processing, can keep the original state at the edge part as much as possible, and finally realizes the measurement of the safe distance from the power transmission line to a camera under the environment of the binocular stereo system through mathematical calculation. Compared with the traditional method for measuring the safe distance of the transmission line, the method has the characteristics of high efficiency, simple algorithm design, high precision and the like, and can realize high-precision calculation of the safe distance measurement of the transmission line of the binocular stereo system.
In addition, the present embodiment also provides a computer device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps in the power line safe distance measuring method of the above embodiment when executing the computer program.
The present embodiment also proposes a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps in the power line safe distance measuring method of the above-described embodiment.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (10)
1. A method for measuring the safe distance of a power transmission line is characterized by comprising the following steps:
s1: calibrating a binocular stereo camera;
s2: acquiring images of different positions of a target power transmission line by using the calibrated binocular stereo camera, wherein the images comprise a left eye image and a right eye image;
s3: preprocessing the image;
s4: inputting the preprocessed image into a convolutional neural network for image feature extraction and classification;
s5: performing feature matching on the image features subjected to extraction and classification by adopting a stereo matching algorithm, so that the same target in the image is matched to obtain a disparity map and a depth map;
s6: and inputting the disparity map and the depth map into a weighted least square filter, filtering out irrelevant parameters, and then performing distance measurement on the disparity map and the depth map subjected to filtering processing by using the disparity formed by a left camera and a right camera to obtain a safe distance measurement result of the power transmission line.
2. A transmission line safety distance measuring method according to claim 1, characterized in that: in the step S1, calibrating the binocular stereo camera includes camera internal reference calibration and external reference calibration, where the internal reference calibration adopts a zhangyingyou calibration method, and the external reference calibration obtains a translation matrix T and a rotation matrix R of the right camera relative to the left camera.
3. A transmission line safety distance measuring method according to claim 2, characterized in that: in the step S3, the specific step of preprocessing the image includes: binocular image calibration, distortion correction and three-dimensional correction.
4. A transmission line safety distance measuring method according to claim 3, characterized in that: the binocular image calibration method specifically comprises the following steps: solving a corresponding relation between a coordinate point in a three-dimensional space and a two-dimensional image coordinate point of the image, solving a transformation matrix of the two-dimensional image coordinate and the physical coordinate of the three-dimensional space obtained by a chessboard board, and then enabling epipolar lines of the left eye image and the right eye image to be on the same horizontal line through the transformation matrix, namely completing binocular image calibration.
5. A transmission line safety distance measuring method according to claim 1, characterized in that: in the step S4, the convolutional neural network includes a convolutional layer, a batch normalization layer, an average pooling layer, an activation layer, a residual layer, an identity mapping layer, a maximum pooling layer, a full-link layer, and a Softmax classifier, which are connected in sequence.
6. A transmission line safety distance measuring method according to claim 1, characterized in that: in the step S5, the step of performing feature matching by using a stereo matching algorithm includes matching cost calculation, cost aggregation, disparity calculation, and disparity optimization.
7. A transmission line safety distance measuring method according to claim 1, characterized in that: in the step S5, the stereo matching algorithm includes a region matching algorithm, a feature matching algorithm, a local matching algorithm, and a global matching algorithm.
8. A transmission line safety distance measuring method according to claim 1, characterized in that: the filtering algorithm adopted in the weighted least square filter in the step S6 includes one of a weighted least square filtering algorithm, a bilateral filtering algorithm, and a guided filtering algorithm.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 8.
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 method of any one of claims 1 to 8.
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| CN112734840A (en) * | 2020-12-31 | 2021-04-30 | 广东电网有限责任公司中山供电局 | Power transmission line safety distance measuring method |
| CN113469947A (en) * | 2021-06-08 | 2021-10-01 | 智洋创新科技股份有限公司 | Method for measuring hidden danger and transmission conductor clearance distance suitable for various terrains |
| CN113947624A (en) * | 2021-10-15 | 2022-01-18 | 苏州智加科技有限公司 | Binocular ranging method, device and storage medium based on neural network |
| CN115861439A (en) * | 2022-12-08 | 2023-03-28 | 重庆市信息通信咨询设计院有限公司 | Depth information measuring method and device, computer equipment and storage medium |
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| CN116819229A (en) * | 2023-06-26 | 2023-09-29 | 广东电网有限责任公司 | A distance measurement method, device, equipment and storage medium for a transmission line |
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