On-line ADSS optical cable defect detection method based on machine vision

Abstract

The invention relates to the technical field of image processing, in particular to an ADSS optical cable defect online detection method based on machine vision, and the method comprises the steps: obtaining an optical cable surface region in an optical cable collection image at each position of an ADSS optical cable; extracting a tracking area in the optical cable acquisition image; obtaining a tracking difference characteristic value according to the area ratio of all tracking areas to the surface area of the optical cable and the gray value difference; extracting a carbon particle region in each tracking region, and obtaining the distribution density of carbon marks according to the areas and spatial distribution conditions of all the carbon particle regions; extracting a skeleton contour in each tracking area; according to the distance and the length between each angular point on the skeleton contour and all adjacent angular points, the tracking intensity is obtained; integrating the tracking difference characteristic value, the carbon tracking distribution density and the tracking intensity to obtain an electric corrosion severity index; and performing anomaly detection analysis on all the electric corrosion severity indexes to obtain a defect detection result. The defect detection precision of the optical cable can be improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to an online detection method for ADSS optical cable defects based on machine vision. Background Art

[0002] ADSS optical cable is a metal-free, tension-resistant, non-inductive, high-insulation, all-dielectric self-supporting optical cable. It is usually used in transmission lines with voltage levels of 35kV and above, providing high-speed, stable, and reliable communication transmission capabilities. During operation, especially in rainy seasons and other conditions with high air humidity, ADSS optical cable will be affected by dry arcs, corona discharges, and other factors, causing electrical corrosion.

[0003] Optical cable defects caused by electrical corrosion seriously affect the stability of the power communication system. In the existing technology, machine learning and deep learning image segmentation methods are usually used to detect defects in ADSS optical cable images; however, the surrounding environment of the optical cable is complex. During the detection of the optical cable image, it is easily affected by the surrounding trees and buildings, causing misjudgment of optical cable defects, resulting in low accuracy in optical cable defect detection. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an online detection method for ADSS optical cable defects based on machine vision to solve the existing problems.

[0005] The machine vision-based ADSS optical cable defect online detection method of this application adopts the following technical solutions:

[0006] An embodiment of the present application provides an ADSS optical cable defect online detection method based on machine vision, the method comprising the following steps:

[0007] S1, obtaining the optical cable acquisition images at various positions of the ADSS optical cable; extracting the optical cable surface area in the optical cable acquisition images;

[0008] S2, for each optical cable acquisition image, extract the electrical trace area according to the grayscale distribution in the optical cable acquisition image; obtain the electrical trace difference characteristic value according to the area ratio of all electrical trace areas to the optical cable surface area and the grayscale value difference;

[0009] S3, extracting the carbon particle area according to the grayscale change in each electrical trace area, and obtaining the carbon trace distribution density of the optical cable collection image according to the area and spatial distribution of all carbon particle areas in the optical cable collection image;

[0010] S4, extracting the skeleton contour in each electric trace area; obtaining the carbonization density index of each electric trace area according to the distance and length between each corner point on the skeleton contour and all adjacent corner points; and obtaining the electric trace intensity of the optical cable collection image by combining the carbonization density index of all electric trace areas in the optical cable collection image;

[0011] S5, comprehensively considering the electrical trace difference characteristic value, carbon trace distribution density and electrical trace intensity of the optical cable acquisition image, and obtaining the electrical corrosion severity index of the optical cable acquisition image; performing anomaly detection and analysis on the electrical corrosion severity index of all optical cable acquisition images, and obtaining the defect detection result of the optical cable.

[0012] Furthermore, the method for obtaining the electrical trace area includes:

[0013] After the grayscale enhancement processing is performed on the optical cable acquisition image, a threshold analysis is performed to obtain a segmentation threshold; the area smaller than the segmentation threshold is regarded as the electrical trace area.

[0014] Furthermore, the method for obtaining the electrical trace difference characteristic value includes:

[0015] Acquire a standard image of an optical cable; extract a surface area of ​​the optical cable in the standard image of the optical cable;

[0016] The electrical trace difference characteristic value of the i-th optical cable acquisition image is recorded as EI i , Where, PA i represents the total area of ​​all electrical traces in the i-th optical cable acquisition image; TA i represents the total area of ​​the optical cable surface in the i-th optical cable acquisition image; G i,j represents the mean gray value of all pixels in the jth electrical trace area in the i-th optical cable acquisition image, G0 represents the mean gray value of all pixels in the optical cable surface area in the optical cable standard image, n represents the number of electrical trace areas, |G i,j -G0| means calculating G i,j -The absolute value of G0.

[0017] Furthermore, the method for acquiring the carbon trace distribution density of the optical cable acquisition image comprises:

[0018] For each electric trace area, each circular area in the electric trace area is extracted and recorded as each carbon particle area;

[0019] The carbon trace distribution density of the i-th optical cable acquisition image is recorded as CP i , In the formula, A i,m represents the area of ​​the mth carbon particle region in the i-th optical cable acquisition image, d i,(m,k)represents the distance between the central pixel points of the mth and kth carbon particle regions in the i-th optical cable acquisition image, where m≠k; s represents the number of carbon particle regions in the i-th optical cable acquisition image.

[0020] Furthermore, the method for obtaining the carbonization density index of each electric trace area includes:

[0021] Obtain corner points on the skeleton contour; record any corner point as a target point; obtain the corner points on each line centered on the target point in the skeleton contour that are closest to the target point, and record them as adjacent corner points of the target point; obtain the skeleton contour between the target point and its adjacent corner points as a carbonization channel between the target point and its adjacent corner points;

[0022] The carbonization density index of the tracking area is obtained according to the length and number of carbonization channels between all corner points in the tracking area and their adjacent corner points.

[0023] Further, the carbonization density index of the electric tracking area is obtained according to the length and number of carbonization channels between all corner points and adjacent corner points in the electric tracking area, including: recording the carbonization density index of the a-th electric tracking area as TC a , Where P a represents the area of ​​the ath electric trace region; l a represents the total length of all carbonized channels in the ath trace region, E a Represents the carbonization channel characteristic number of all corner points in the ath electric tracking area.

[0024] Furthermore, the characteristic number of carbonized channels at all corner points in the trace area is: the average number of connected carbonized channels at all corner points in the trace area.

[0025] Furthermore, the method for obtaining the electrical trace strength of the optical cable acquisition image includes:

[0026] The average value of the carbonization density index of all electrical trace areas in the optical cable acquisition image is calculated as the electrical trace intensity of the optical cable acquisition image.

[0027] Furthermore, the method for obtaining the electrical corrosion severity index of the optical cable acquisition image includes:

[0028] The sum of the electric trace intensity and the carbon trace distribution density of each optical cable acquisition image is calculated; and the electric trace difference characteristic value of each optical cable acquisition image and the sum are combined to obtain the electrical corrosion severity index of each optical cable acquisition image.

[0029] Furthermore, the detection result of the electrical corrosion defect of the optical cable includes:

[0030] Anomaly detection is performed on the electrocorrosion severity index of all optical cable acquisition images to obtain an anomaly score for each optical cable acquisition image; optical cable acquisition images with an anomaly score greater than a preset anomaly threshold are recorded as optical cable defect images; and the acquisition position of the optical cable defect image is taken as the occurrence position of the electrocorrosion defect on the optical cable.

[0031] This application has at least the following beneficial effects:

[0032] The present application analyzes the characteristics of the formation of electrocorrosion defects of optical cables. First, the grayscale changes of the optical cable surface in the optical cable acquisition image when the optical cable is electrocorroded are analyzed. The electric trace severity coefficient is constructed based on the area of ​​the electric trace area and the grayscale value difference. The color and brightness changes of the optical cable surface when the electric corrosion phenomenon occurs on the optical cable surface are considered, and the severity of the electric corrosion phenomenon on the optical cable surface is preliminarily evaluated. The characteristics of the traces of carbon particles deposited on the surface of the optical cable during the electrocorrosion process are further analyzed. The carbon trace distribution density is constructed based on the area and spatial distribution of the carbon particle area to accurately judge the severity of the electrocorrosion. Further, the shape characteristics of the carbonization channel formed during the electrocorrosion process are analyzed, and the carbonization density index is calculated, which can accurately reflect the distribution density of the carbonization channel. The greater the electric trace intensity, the more severe the electrocorrosion phenomenon in the area. Finally, the electric trace difference characteristic value, the carbon trace distribution density and the electric trace intensity are combined to obtain the electric corrosion severity index, and the severity of the electric corrosion phenomenon on the surface of each section of the optical cable is accurately judged. The defect detection result of the optical cable is obtained according to the electric corrosion severity index, which fully considers the characteristics of the electric corrosion defect, can eliminate the interference of environmental factors, and improves the defect detection accuracy of the optical cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flowchart of the steps of the online detection method of ADSS optical cable defects based on machine vision provided in this application;

[0035] Figure 2 A schematic diagram of selecting adjacent corner points provided for one embodiment of the present application. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0037] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0038] The specific scheme of the online detection method of ADSS optical cable defects based on machine vision provided by the present application is described in detail below with reference to the accompanying drawings.

[0039] An embodiment of the present application provides an ADSS optical cable defect online detection method based on machine vision. Specifically, the following ADSS optical cable defect online detection method based on machine vision is provided. Figure 1 , the method comprises the following steps:

[0040] S1, obtaining the optical cable acquisition images at various positions of the ADSS optical cable; extracting the optical cable surface area in the optical cable acquisition images.

[0041] A specific implementation scenario of the embodiment of the present application is the detection scenario of electrical corrosion defects of ADSS optical cables; for ADSS optical cables that work in outdoor environments for a long time, as time accumulates, soluble salt substances and dust in the air will accumulate on the surface of the optical cable. After continuous erosion by moisture in the air, the soluble salt substances attached to the outer surface of the optical cable will form a resistance layer. Driven by the induced voltage, a discharge arc will be generated. The arc will discharge repeatedly. The heat generated by the resistance layer will cause the cable protective sheath to form dendritic carbonized electrical traces, thereby destroying the polymer of the cable protective sheath and forming electrical corrosion. The method of defect detection on the image of the ADSS optical cable is usually used for electrical corrosion defect detection.

[0042] Since ADSS optical cables often extend for thousands of meters in actual applications, it is impossible to obtain the entire surface image of the optical cable at one time. Therefore, in this embodiment, a high-resolution camera is used to continuously obtain the optical cable acquisition images of the outdoor optical cable in sections under good light conditions. In the process of image acquisition, there will inevitably be certain interference noises, which will reduce the quality of the image and affect the accuracy of ADSS optical cable defect detection. Therefore, in order to reduce the interference of noise, the acquired optical cable standard image and optical cable acquisition image are subjected to image denoising by using median filtering. Median filtering is a well-known technology, and the specific implementation process will not be repeated.

[0043] When acquiring the surface image of the optical cable, background information, such as the earth, trees, etc., will inevitably be included. In order to prevent the background information from interfering with the accuracy of optical cable defect detection, the optical cable needs to be extracted from the entire image. This embodiment uses a neural network to extract the optical cable area from the acquired optical cable acquisition image. The training details of the neural network are as follows: 2000 optical cable surface acquisition images are acquired to mark the optical cable surface area, and normalized, that is, the image matrix is ​​converted into a floating point number between [0,1], so that the model can converge better. The neural network uses a convolutional neural network for image segmentation training, using ResNet18 and the network structure, and the activation function uses the Softmax function, and finally a trained neural network model is obtained.

[0044] The currently acquired optical cable acquisition image and the optical cable standard image are input into the trained neural network model to obtain the optical cable surface area in the optical cable acquisition image and the optical cable standard image. In this embodiment, in order to eliminate the interference of environmental factors and accurately detect electrical corrosion defects, the characteristics of the electrical corrosion phenomenon in the optical cable acquisition image are fully analyzed.

[0045] S2, for each optical cable acquisition image, extract the electrical trace area according to the grayscale distribution in the optical cable acquisition image; obtain the electrical trace difference characteristic value according to the area ratio of all electrical trace areas to the optical cable surface area and the grayscale value difference.

[0046] Due to the different external manifestations of the arc on the outer protective sheath of the optical cable during the electro-corrosion process, there are usually three common forms of electro-corrosion: corrosion, electric trace, and breakdown. This application mainly focuses on defect detection of the electric trace of the outer protective sheath of the optical cable. The electric trace refers to the formation of a dendritic carbonization channel on the outer protective sheath of the optical cable under the action of the electric arc. As the degree of carbonization deepens, the outer sheath cracks under the action of the tension of the optical cable.

[0047] In order to extend the service life of the optical cable, the outer sheath of the ADSS optical cable is usually dark in color. However, when the optical cable is electrocorroded, the color of the optical cable will change significantly. Due to the heating phenomenon caused by the current on the surface of the optical cable, the outer sheath material of the optical cable is oxidized or partially degraded, so that the color of the outer sheath of the optical cable in the electrical trace area becomes darker and dull.

[0048] Since the electrical trace area is usually darker and dull, it will appear darker in the grayscale image. In order to identify the electrical trace area in the optical cable acquisition image, each optical cable acquisition image is subjected to grayscale enhancement processing and then a threshold analysis is performed to obtain a segmentation threshold; the area smaller than the segmentation threshold is regarded as the electrical trace area.

[0049] It should be noted that grayscale enhancement of an image can improve the contrast of the image and facilitate the identification of objects in the image; in this embodiment, a histogram equalization algorithm is used to enhance the grayscale of the optical cable acquisition image, further amplifying the brightness difference between the electrical trace area and the surrounding normal black area. As other embodiments of the present application, other image enhancement algorithms may be selected, and this application does not limit them.

[0050] Furthermore, in this embodiment, the Otsu method is used to perform threshold analysis on the optical cable acquisition image after grayscale enhancement processing to obtain a segmentation threshold; since the color of the electrical trace area is obviously different from other areas and the color is darker, the area smaller than the segmentation threshold is taken as the electrical trace area.

[0051] Based on the above analysis, the grayscale value of the electrical trace area is lower than that of the normal optical cable area, and the degree of damage to the surface of the optical cable caused by electrical traces is related to the area and grayscale value of the electrical trace area. First, under the same acquisition equipment conditions as the optical cable acquisition image, an image of a section of ADSS optical cable with no surface defects is obtained as the optical cable standard image; wherein, the optical cable acquisition image and the optical cable standard image are both grayscale images; the optical cable surface area in the optical cable standard image is obtained using the same acquisition method as the optical cable surface area in the optical cable acquisition image. Furthermore, based on the area proportion of all electrical trace areas to the optical cable surface area, and the grayscale value differences between different electrical trace areas, the electrical trace difference characteristic value is obtained.

[0052] Specifically, in this embodiment, the electrical trace difference characteristic value of the i-th optical cable acquisition image is recorded as EI i , Where PA i represents the total area of ​​all electrical traces in the i-th optical cable acquisition image; TA i represents the total area of ​​the optical cable surface in the i-th optical cable acquisition image; the above areas are all represented by the number of pixels; G i,j represents the mean gray value of all pixels in the jth electrical trace area in the i-th optical cable acquisition image, G0 represents the mean gray value of all pixels in the optical cable surface area in the optical cable standard image, n represents the number of electrical trace areas, |G i,j -G0| means calculating G i,j -The absolute value of G0.

[0053] When electrical corrosion occurs on the surface of the optical cable, the area of ​​the electrical trace area accounts for the proportion of the total area of ​​the entire optical cable area. The larger the value, the gray value of the cable surface will change when electrical corrosion occurs on the cable surface. The greater the difference between the gray value and the average gray value of the cable area in the standard cable image, the larger the calculated electrical trace difference characteristic value EI iThe larger the value, the more serious the electrical corrosion on the cable surface.

[0054] S3, extracting the carbon particle area according to the grayscale change in each electric trace area, and obtaining the carbon trace distribution density of the optical cable collection image according to the area and spatial distribution of all carbon particle areas in the optical cable collection image.

[0055] During the electrical corrosion of optical cables, arc discharge caused by the electric field will generate high temperature, causing physical and chemical changes in the outer protective layer of the optical cable, including but not limited to melting, gasification and carbonization. These processes will release carbon particles, which will be deposited on the surface or inside of the optical cable, forming carbon particle traces.

[0056] Therefore, for each electrical trace area, each circular area in the electrical trace area is first extracted and recorded as each carbon particle area; in this embodiment, the Hough circle detection algorithm is used to extract the carbon particle area; as other embodiments of the present application, a circular template matching algorithm can be selected to match in the electrical trace area to obtain the carbon particle area, which is not limited in this application. Further, according to the distribution and area of ​​the carbon particles, the carbon trace distribution density on the surface of the optical cable is obtained.

[0057] Carbon particles exist in the form of spheres on the surface of the outer protective sheath of the optical cable. The more carbon particle traces there are and the denser the distribution, the more severe the corrosion on the surface of the optical cable is.

[0058] The carbon trace distribution density of the i-th optical cable acquisition image is recorded as CP i , the calculation formula is:

[0059]

[0060] In the formula, A i,m represents the area of ​​the mth carbon particle region in the i-th optical cable acquisition image, d i,(m,k) represents the distance between the central pixel points of the mth and kth carbon particle regions in the i-th optical cable acquisition image, where m≠k, and the Euclidean distance is used for distance calculation in this embodiment; s represents the number of carbon particle regions in the i-th optical cable acquisition image.

[0061] When carbon particle traces appear on the surface of the outer protective sheath of the optical cable, the larger the area of ​​the carbon particle region and the smaller the distance between the carbon particle regions, the larger the area of ​​the carbon particles and the denser the distribution, which makes the calculated carbon mark distribution density larger, indicating that the outer protective sheath of the optical cable is more severely eroded by carbon particles.

[0062] S4, extracting the skeleton contour in each electric trace area; obtaining the carbonization density index of each electric trace area according to the distance and length between each corner point on the skeleton contour and all adjacent corner points; and obtaining the electric trace intensity of the optical cable acquisition image by combining the carbonization density index of all electric trace areas in the optical cable acquisition image.

[0063] In the discharge channel, due to the concentrated effect of arc discharge, the deposition of carbon particles is more significant, forming a carbonized layer. With the repeated occurrence and continuous action of arc discharge, carbon particles continue to accumulate on the inner wall of the discharge channel, forming a carbonized channel. Since the position and direction of the discharge arc on the surface of the outer protective sheath of the optical cable are not fixed, the formation of the carbonized channel usually presents a dendritic characteristic. These dendritic carbonized channels interweave and expand with each other, eventually forming a complete carbonized channel. Therefore, the denser the carbonized channels on the surface of the outer protective sheath of the optical cable, the more serious the electrical corrosion phenomenon in this area.

[0064] In order to extract the carbonization channel, in this embodiment, the skeleton contour in each electric trace area is extracted; the corner points on the skeleton contour are obtained; specifically, in this embodiment, the skeleton contour of the carbonization channel in the image is extracted using the Zhang-Suen algorithm for the extracted electric trace area, and the corner points of the carbonization channel are extracted and detected using the Harris algorithm for the skeleton contour of the obtained carbonization channel. The Zhang-Suen algorithm and the Harris algorithm are well-known technologies, and the specific implementation process will not be repeated; the implementer can select the skeleton contour extraction algorithm and the corner point detection method according to the actual situation, and this application does not limit it.

[0065] Dendritic carbonization channels have complex shapes and branch structures, and each intersection may connect multiple carbonization channels. Therefore, any corner point is recorded as the target point; the corner points closest to the target point on each line centered on the target point in the skeleton contour are obtained and recorded as the adjacent corner points of the target point; the skeleton contour between the target point and its adjacent corner points is obtained as the carbonization channel between the target point and its adjacent corner points.

[0066] In this embodiment, the pixel points around each corner point are traversed, and starting from the corner point, the pixel points along the skeleton contour are tracked until the next corner point is encountered, which is an adjacent corner point, which may be an intersection point or the end point of the skeleton contour; Figure 2 As shown, the adjacent corner points of corner point A are A1, A2 and A3.

[0067] Furthermore, according to the above analysis, the dendritic characteristics of the carbonization channel, the more divergent the branches, the denser the branches, the more serious the electrocorrosion phenomenon in the area. According to the length and number of carbonization channels between all corner points in the electrical trace area and their adjacent corner points, the carbonization density index of the electrical trace area is obtained.

[0068] As an embodiment of the present application, the number of pixel points on the skeleton contour passed during the tracking process is recorded as the length of the carbonization channel. For each corner point, all carbonization channels connected to it are recorded and identified, and finally the number of carbonization channels connected to each corner point and the length of each carbonization channel are counted. Based on the above analysis, the carbonization density index is constructed: the carbonization density index of the ath electric trace area is recorded as TCa , Where P a represents the area of ​​the ath electric trace region; l a represents the total length of all carbonized channels in the ath trace region, E a Represents the carbonization channel characteristic number of all corner points in the ath electric tracking area.

[0069] The characteristic number of carbonization channels of all corner points in the trace area reflects the number of carbonization channels connected to each corner point; the more connected carbonization channels are, the denser the distribution of carbonization channels is; in this embodiment, the characteristic number of carbonization channels of all corner points in the trace area is: the mean of the number of connected carbonization channels of all corner points in the trace area; in other embodiments of the present application, the mode of the number of connected carbonization channels of all corner points in the trace area can be selected as the characteristic number of carbonization channels of all corner points in the trace area.

[0070] When electrical corrosion occurs on the surface of the optical cable, the longer the length of the carbonized channel per unit area in the electrical trace area is, the denser the carbonized channels are. At this time, the more carbonized channels are connected to each corner point, the more branches of the carbonized channels there are. As a result, the larger the calculated electrical trace density index is, the denser the distribution of the carbonized channels is, indicating that the electrical corrosion phenomenon on the surface of the optical cable is more serious.

[0071] Therefore, in order to evaluate the distribution of electrical traces in the optical cable acquisition image as a whole, the average carbonization density index of all electrical trace areas in the optical cable acquisition image is calculated as the electrical trace intensity of the optical cable acquisition image. The average carbonization density index of all electrical trace areas in the optical cable acquisition image is used to indicate the intensity of electrical traces in the optical cable acquisition image. The greater the electrical trace intensity, the more likely it is to cause serious corrosion to the optical cable surface.

[0072] S5, comprehensively considering the electrical trace difference characteristic value, carbon trace distribution density and electrical trace intensity of the optical cable acquisition image, and obtaining the electrical corrosion severity index of the optical cable acquisition image; performing anomaly detection and analysis on the electrical corrosion severity index of all optical cable acquisition images, and obtaining the defect detection result of the optical cable.

[0073] Through the above analysis, the electric traces formed by the electric corrosion on the surface of the optical cable mainly go through two stages: repeated arc discharge to form electric traces and high temperature causes physical and chemical changes in the outer protective layer of the optical cable to form carbon particle traces; therefore, according to the electric trace intensity and carbon trace distribution density calculated above, the electric corrosion phenomenon on the surface of the optical cable can be characterized. Combined with the electric corrosion characteristics initially reflected by the electric trace difference characteristic value calculated above, the severity of the electric corrosion phenomenon on the surface of the optical cable can be comprehensively evaluated.

[0074] Specifically, the sum of the electric trace intensity and the carbon trace distribution density of each optical cable acquisition image is calculated; and the electric trace difference characteristic value of each optical cable acquisition image and the sum are fused to obtain the electrical corrosion severity index of each optical cable acquisition image.

[0075] It should be noted that fusion means that the fused data and the fused result have a same-direction change relationship, that is, the larger the fused data, the larger the fused result. The specific form of fusion is determined by the actual application, and this application does not impose any special restrictions.

[0076] In this embodiment, the electrical corrosion severity index of the i-th optical cable acquisition image is recorded as SI i , SI i =EI i ×(TC i +CP i );where CP i represents the carbon trace distribution density of the i-th optical cable acquisition image; EI i represents the electrical trace difference characteristic value of the i-th optical cable acquisition image; TC i Represents the electrical trace intensity of the i-th optical cable acquisition image.

[0077] As another embodiment of the present application, the electrical corrosion severity index SI of the i-th optical cable acquisition image is i =EI i ×exp(TC i +CP i ), where exp() is an exponential function with the natural constant e as the base, which is used to amplify the importance of the electrical trace intensity and carbon trace distribution density for calculating the electrical corrosion severity index.

[0078] When carbon particle traces appear on the surface of the outer protective sheath of the optical cable, the greater the intensity of the electric trace, the easier it is to cause serious corrosion to the surface of the optical cable; at this time, the larger the area of ​​the carbon particles, the denser the distribution, and the greater the distribution density of the carbon traces, the more serious the carbon particle erosion of the outer protective sheath of the optical cable is, and the larger the electric trace difference characteristic value, the more significant the electric corrosion characteristics, the greater the electric corrosion severity index, and the more serious the electric corrosion phenomenon of the optical cable acquisition image.

[0079] After the above analysis, the electrocorrosion severity index of each optical cable acquisition image can be obtained. When an optical cable is electrocorroded, it is often concentrated in a certain section or certain specific areas, rather than the entire optical cable being electrocorroded at the same time. Therefore, there is a large difference in the electrocorrosion severity index of the optical cable with electrocorrosion and other normal optical cables; therefore, the electrocorrosion severity index of all optical cable acquisition images is detected for anomalies, and the anomaly score of each optical cable acquisition image is obtained; the optical cable acquisition image with an anomaly score greater than the preset anomaly threshold γ = 0.8 is recorded as an optical cable defect image. The optical cable defect image corresponds to the optical cable acquisition image with severe electrocorrosion.

[0080] In this embodiment, the HBOS algorithm is used to perform anomaly detection on the electrical corrosion severity index of all optical cable acquisition images; as other embodiments of the present application, the LOF anomaly detection algorithm can be selected, and this application does not limit it.

[0081] Finally, the location where the optical cable defect image is collected is the location where the electrical corrosion defect occurs on the ADSS optical cable. The larger the anomaly score, the more severe the electrical corrosion phenomenon.

[0082] Through the above description of the implementation method in combination with the accompanying drawings, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0083] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. The ADSS optical cable defect online detection method based on machine vision is characterized by: The method comprises the following steps: S1, obtaining the optical cable acquisition images at various positions of the ADSS optical cable; extracting the optical cable surface area in the optical cable acquisition images; S2, for each optical cable acquisition image, extract the electrical trace area according to the grayscale distribution in the optical cable acquisition image; obtain the electrical trace difference characteristic value according to the area ratio of all electrical trace areas to the optical cable surface area and the grayscale value difference; S3, extracting the carbon particle area according to the grayscale change in each electrical trace area, and obtaining the carbon trace distribution density of the optical cable collection image according to the area and spatial distribution of all carbon particle areas in the optical cable collection image; S4, extracting the skeleton contour in each electric trace area; obtaining the carbonization density index of each electric trace area according to the distance and length between each corner point on the skeleton contour and all adjacent corner points; and obtaining the electric trace intensity of the optical cable collection image by combining the carbonization density index of all electric trace areas in the optical cable collection image; S5, comprehensively considering the electrical trace difference characteristic value, carbon trace distribution density and electrical trace intensity of the optical cable acquisition image, and obtaining the electrical corrosion severity index of the optical cable acquisition image; performing anomaly detection and analysis on the electrical corrosion severity index of all optical cable acquisition images, and obtaining the defect detection result of the optical cable.

2. The ADSS optical cable defect online detection method based on machine vision as claimed in claim 1, characterized in that: The method for obtaining the electric trace area includes: After the grayscale enhancement processing is performed on the optical cable acquisition image, a threshold analysis is performed to obtain a segmentation threshold; the area smaller than the segmentation threshold is regarded as the electrical trace area.

3. The ADSS optical cable defect online detection method based on machine vision as claimed in claim 1, characterized in that: The method for obtaining the electrical trace difference characteristic value comprises: Acquire a standard image of an optical cable; extract a surface area of ​​the optical cable in the standard image of the optical cable; The electrical trace difference characteristic value of the i-th optical cable acquisition image is recorded as EI i , Where, PA i represents the total area of ​​all electrical traces in the i-th optical cable acquisition image; TA i represents the total area of ​​the optical cable surface in the i-th optical cable acquisition image; G i,j represents the mean gray value of all pixels in the jth electrical trace area in the i-th optical cable acquisition image, G0 represents the mean gray value of all pixels in the optical cable surface area in the optical cable standard image, n represents the number of electrical trace areas, |G i,j -G0| means calculating G i,j -The absolute value of G0.

4. The ADSS optical cable defect online detection method based on machine vision as claimed in claim 1, characterized in that: The method for acquiring the carbon trace distribution density of the optical cable acquisition image comprises: For each electric trace area, each circular area in the electric trace area is extracted and recorded as each carbon particle area; The carbon trace distribution density of the i-th optical cable acquisition image is recorded as CP i , In the formula, A i,m represents the area of ​​the mth carbon particle region in the i-th optical cable acquisition image, d i,(m,k) represents the distance between the central pixel points of the mth and kth carbon particle regions in the i-th optical cable acquisition image, where m≠k; s represents the number of carbon particle regions in the i-th optical cable acquisition image.

5. The ADSS optical cable defect online detection method based on machine vision as claimed in claim 1, characterized in that: The method for obtaining the carbonization density index of each electric trace area includes: Obtain corner points on the skeleton contour; record any corner point as a target point; obtain the corner points on each line centered on the target point in the skeleton contour that are closest to the target point, and record them as adjacent corner points of the target point; obtain the skeleton contour between the target point and its adjacent corner points as a carbonization channel between the target point and its adjacent corner points; The carbonization density index of the tracking area is obtained according to the length and number of carbonization channels between all corner points in the tracking area and their adjacent corner points.

6. The method for online detection of ADSS optical cable defects based on machine vision as claimed in claim 5, characterized in that: The carbonization density index of the electric tracking area is obtained according to the length and number of carbonization channels between all corner points and adjacent corner points in the electric tracking area, including: recording the carbonization density index of the a-th electric tracking area as TC a , Where P a represents the area of ​​the ath electric trace region; l a represents the total length of all carbonized channels in the ath trace region, E a Represents the carbonization channel characteristic number of all corner points in the ath electric tracking area.

7. The ADSS optical cable defect online detection method based on machine vision as claimed in claim 6, characterized in that: The characteristic number of carbonized channels at all corner points in the trace area is: the average number of connected carbonized channels at all corner points in the trace area.

8. The ADSS optical cable defect online detection method based on machine vision as claimed in claim 1, characterized in that: The method for obtaining the electrical trace strength of the optical cable acquisition image comprises: The average value of the carbonization density index of all electrical trace areas in the optical cable acquisition image is calculated as the electrical trace intensity of the optical cable acquisition image.

9. The ADSS optical cable defect online detection method based on machine vision according to claim 1, characterized in that: The method for obtaining the electrical corrosion severity index of the optical cable acquisition image comprises: The sum of the electric trace intensity and the carbon trace distribution density of each optical cable acquisition image is calculated; and the electric trace difference characteristic value of each optical cable acquisition image and the sum are combined to obtain the electrical corrosion severity index of each optical cable acquisition image.

10. The ADSS optical cable defect online detection method based on machine vision according to claim 1, characterized in that: The detection results of the electrical corrosion defects of the optical cable include: Anomaly detection is performed on the electrocorrosion severity index of all optical cable acquisition images to obtain an anomaly score for each optical cable acquisition image; optical cable acquisition images with an anomaly score greater than a preset anomaly threshold are recorded as optical cable defect images; and the acquisition position of the optical cable defect image is taken as the occurrence position of the electrocorrosion defect on the optical cable.

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