ADSS optical cable defect automatic identification method and device

Abstract

The present application relates to the field of image processing technology, and specifically to an ADSS optical cable defect automatic identification method and device, the method comprising: obtaining an optical cable image to be detected, performing feature point detection, obtaining a first defect feature value based on the difference in grayscale value between each feature point and the pixel points in the local range, combined with the discrete characteristics of the grayscale value of the pixel points in the local range; obtaining a grayscale fluctuation value based on the variation characteristics of the grayscale values ​​between adjacent pixels in each row of the optical cable image to be detected, analyzing the difference between the grayscale fluctuation values ​​between each row and the adjacent rows to obtain a defect judgment value; obtaining a second defect feature value based on the grayscale run matrix of the optical cable image to be detected; and performing defect identification on the optical cable image to be detected based on the two defect feature values ​​and the defect characteristics in historical optical cable images. The present application aims to improve the accuracy of optical cable defect identification by analyzing the characteristics of optical cable defects and determining thresholds applicable to different defect types.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method and device for automatically identifying defects in ADSS optical cables. Background Art

[0002] With technological advancements, fiber-optic communication technology has become one of the mainstays of modern communications, playing a vital role in modern communication networks. However, during the production and use of optical fiber cables, various factors can cause various surface defects, which can affect the product's lifespan. To ensure the quality of optical fiber communications, fiber optic cable defect identification is often necessary.

[0003] Defect extraction methods based on threshold segmentation are widely used for defect identification due to their computational simplicity and high efficiency. However, optical cable defects vary greatly, such as pinholes and neckings. Fixed thresholds are difficult to adapt to these variations, making the fixed thresholds used in conventional threshold segmentation methods inaccurate in distinguishing different defect types, thus affecting the accuracy of the overall identification results. Summary of the Invention

[0004] In view of the above, it is necessary to provide an automatic identification method and device for ADSS optical cable defects to solve the above problems.

[0005] The first aspect of the present application provides a method for automatically identifying defects in an ADSS optical cable, the method comprising:

[0006] Acquire an image of the optical cable to be inspected, wherein the optical cable to be inspected is placed horizontally, and its axis is parallel to both the upper and lower edge lines of the image of the optical cable to be inspected;

[0007] Perform feature point detection on the optical cable image to be inspected, and obtain the first defect feature value of each feature point based on the difference in grayscale value between each feature point and the pixel points in the local range, combined with the discrete features of the grayscale values ​​of the pixel points in the local range;

[0008] Based on the grayscale value variation characteristics between adjacent pixels in each row of the optical cable image to be inspected, the grayscale fluctuation value of each row is obtained; based on the difference between the grayscale fluctuation values ​​of each row and its adjacent rows, the defect judgment value of each row is obtained; based on the grayscale fluctuation value and defect judgment value of the row where each feature point is located, combined with the grayscale run-length matrix of the optical cable image to be inspected, the second defect feature value of each feature point is obtained;

[0009] According to the first defect characteristic value and the second defect characteristic value, combined with the defect characteristics in the historical optical cable image, defect thresholds corresponding to various defect characteristic values ​​are obtained, and defects are identified on the optical cable image to be inspected.

[0010] The first defect characteristic value of each characteristic point is obtained as follows:

[0011] The first defect discrimination value of each feature point is obtained based on the overall grayscale difference between each feature point and the pixels in its neighborhood, combined with the discrete degree of the grayscale values ​​of the pixels in the row where each feature point is located;

[0012] The first defect discrimination value is positively correlated with the difference and the discrete degree; the first defect discrimination value is negatively correlated with the grayscale value of the corresponding feature point.

[0013] The grayscale fluctuation value of each row is obtained as follows:

[0014] For each row of the optical cable image to be inspected, a first-order difference sequence of each row is obtained based on the grayscale value, and the difference between the number of positive numbers and the number of negative numbers in the first-order difference sequence is counted. Combined with the number of non-zero elements in the first-order difference sequence, the grayscale fluctuation value of each row is obtained.

[0015] The defect judgment value of each row is obtained as follows:

[0016] According to the fusion result of the difference in grayscale fluctuation value between each row and adjacent rows of the optical cable image to be inspected, the defect judgment value of each row is obtained; wherein the defect judgment value is negatively correlated with the fusion result.

[0017] The process of obtaining the second defect characteristic value of each characteristic point includes:

[0018] Obtain the direction value of each feature point during the feature point detection process; obtain the contrast value of each feature point based on the position characteristics of the direction value of each feature point in the vertical direction;

[0019] Scan the optical cable image to be inspected in a vertical direction to obtain a grayscale run-length matrix of the optical cable image to be inspected;

[0020] The second defect characteristic value of the i-th characteristic point is recorded as H i , its formula form is: Among them, D i Indicates the defect judgment value of the row where the i-th feature point is located; Indicates the number of non-zero values ​​in the row corresponding to the gray level of the i-th feature point in the gray run matrix; Δ i Represents the contrast value of the i-th feature point.

[0021] The specific process of obtaining the contrast value of each feature point is as follows:

[0022] Two feature points with the same ordinate as each feature point and the largest direction value are obtained, and the absolute value of the difference between the abscissas of the two feature points in the optical cable image to be detected is used as the comparison value of each feature point.

[0023] The steps of obtaining the defect thresholds corresponding to various defect characteristic values ​​are as follows:

[0024] Obtain a preset number of historical optical cable defect images;

[0025] According to the distribution of various defect feature values ​​in each historical optical cable defect image, a segmentation threshold value corresponding to the defect feature value of each historical optical cable defect image is obtained;

[0026] According to the average level of the segmentation thresholds of various defect feature values ​​of all optical cable defect images, defect thresholds of various defect feature values ​​of the optical cable defect image to be detected are obtained; wherein the defect thresholds include a first defect threshold and a second defect threshold.

[0027] The segmentation threshold for obtaining the defect feature value corresponding to each historical optical cable defect image is specifically:

[0028] For any defect feature value, threshold segmentation is performed on the any defect feature value of all feature points of each optical cable defect image to obtain a segmentation threshold of the any defect feature value of each optical cable defect image.

[0029] The process of identifying defects in the optical cable image to be inspected includes:

[0030] When the average value of all the first defect feature values ​​in the image of the optical cable to be inspected is greater than the first defect threshold, and the average value of all the second defect feature values ​​is greater than the second defect threshold, the defect identification result of the optical cable to be inspected is that a pinhole defect and a necking defect exist;

[0031] When the average value of all the first defect feature values ​​in the image of the optical cable to be inspected is greater than the first defect threshold, and the average value of all the second defect feature values ​​is less than or equal to the second defect threshold, the defect identification result of the optical cable to be inspected is a pinhole defect;

[0032] When the mean value of all the first defect feature values ​​in the image of the optical cable to be inspected is less than or equal to the first defect threshold, and the mean value of all the second defect feature values ​​is greater than the second defect threshold, the defect identification result of the optical cable to be inspected is a necking defect; otherwise, the defect identification result of the optical cable to be inspected is that neither of these two defects exists.

[0033] In a second aspect, an embodiment of the present application also provides an ADSS optical cable defect automatic identification device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0034] In the above scheme, the image of the optical cable to be detected is first obtained, and feature point detection is performed on it. According to the grayscale difference between the feature point and the nearby pixel points, and the discrete characteristics of the grayscale values ​​of the nearby pixel points, a first feature defect value is constructed to characterize the characteristics of the small hole defect in the optical cable image. Its beneficial effect is that for each feature point in the optical cable image to be detected, the possibility of it falling into the small hole defect area is analyzed, thereby improving the accuracy of corresponding defect recognition; further, the grayscale change analysis is performed on each row in the optical cable image to be detected, and the difference in grayscale changes between adjacent rows is performed to obtain the grayscale fluctuation value and defect judgment value of each row. Its beneficial effect is that through the grayscale fluctuation value, it is judged whether there is a necking defect in the optical cable image to be detected based on the change of texture features, and further through the defect judgment value The texture features of each row are compared. If the texture fluctuation features are obvious and have similar fluctuation features, it can be preliminarily determined that there is a defect at the position corresponding to the feature point; the grayscale fluctuation value and the defect judgment value are combined with the grayscale run matrix to construct a second defect feature value. Its beneficial effect is that it comprehensively considers the texture features and shape features of the necking defect in the optical cable, more accurately describes the defect features, and makes the identification of defects in the optical cable to be inspected more accurate; the two defect features of the historical optical cable defect image are analyzed. Its beneficial effect is that different defect thresholds are obtained for different defects, which solves the problem that the same threshold cannot effectively distinguish these different types of defects, thereby making the defect identification results of the optical cable image to be inspected more accurate and the efficiency of defect identification higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of the steps of the automatic identification method of ADSS optical cable defects provided in one embodiment of the present application;

[0036] Figure 2 A flowchart for obtaining a defect threshold is provided for one embodiment of the present application. DETAILED DESCRIPTION

[0037] In the description of the embodiments of this application, words such as "exemplary," "or," and "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary," "or," and "for example" is intended to present the relevant concepts in a concrete manner.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the art of this application. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0039] It should also be noted that the terms "first" and "second" in this application and the accompanying drawings are used to distinguish similar objects, rather than to describe a specific order or sequence. The methods disclosed in the embodiments of this application or the methods shown in the flowcharts include one or more steps for implementing the methods. Without departing from the scope of protection of this application, the order of executing multiple steps can be interchanged with each other, and some steps can also be deleted.

[0040] 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.

[0041] The specific scheme of the ADSS optical cable defect automatic identification method and device provided by this application is described in detail below with reference to the accompanying drawings.

[0042] See also Figure 1 , which shows a flowchart of the steps of the automatic identification method of ADSS optical cable defects provided by an embodiment of the present application, the method comprising the following steps:

[0043] The first step is to obtain an image of the optical cable to be inspected. The optical cable to be inspected is placed horizontally, and its axis is parallel to the upper and lower edge lines of the image of the optical cable to be inspected.

[0044] A CCD camera is used to capture ADSS optical cable images. The acquired ADSS optical cable images are rectangular. During the acquisition process, the optical cable is kept horizontal in the image, and the central axis of the optical cable is parallel to the upper and lower edges of the ADSS optical cable image. In this embodiment, the ADSS optical cable image is converted into grayscale, an adaptive histogram equalization algorithm is used to enhance the image, and a median filtering algorithm is used to denoise the image to obtain an optical cable image to be detected; wherein, the adaptive histogram equalization algorithm and the median filtering algorithm are both existing publicly known technologies, and this application will not elaborate on them; in addition, the implementer may choose other methods for image processing, and this application does not impose any restrictions on this.

[0045] The second step is to perform feature point detection on the optical cable image to be inspected. Based on the difference in grayscale value between each feature point and the pixel points in the local range, combined with the discrete features of the grayscale value of the pixel points in the local range, the first defect feature value of each feature point is obtained.

[0046] When detecting defects in optical cable images, traditional threshold segmentation methods rely on comparing the segmentation threshold with the pixel grayscale value to identify defective areas. However, grayscale values ​​alone are not sufficient to accurately distinguish defective from non-defective areas. This is because the grayscale values ​​in optical cable images can be affected by various factors, resulting in similar grayscale appearances in defective and normal areas, making defect identification in the optical cable images inaccurate. To address this issue, we first perform feature point detection on the optical cable image to be inspected. We analyze the pinhole defect characteristics in the optical cable image to obtain the first defect feature value for each feature point.

[0047] In this embodiment, the Speeded-Up Robust Features (SURF) algorithm is used to detect feature points of the optical cable image to obtain a direction assignment for each feature point. The SURF algorithm is a well-known technology and is not described in detail in this application. In addition, the implementer can select a suitable feature point detection algorithm to obtain feature points and their direction assignments, and this application does not impose any restrictions on this.

[0048] Among all the feature points obtained, there are both feature points in non-defective areas and feature points in defective areas, so these feature points need to be further distinguished to obtain feature points in the defective areas. For pinhole-type defects, the color of the feature point at the pinhole is darker, while the color around the pinhole is relatively lighter. Therefore, the grayscale value of the pinhole feature point is smaller, and the grayscale value of the pinhole feature point will be different from the grayscale value of the pixels surrounding the pinhole feature point. In addition, due to the influence of light, reflective areas will appear on the surface of the optical cable, while the pinhole feature point will not reflect light. When the pinhole is located in a reflective area, there will be a large grayscale difference in the pixels in the row where the pinhole feature point is located.

[0049] Taking each feature point as the center, the neighborhood of each feature point is obtained; according to the difference between the average level of the grayscale values ​​of all pixels in the neighborhood of each feature point and the grayscale value of the corresponding feature point, combined with the grayscale value discreteness of the pixels in the row where each feature point is located, the first defect discrimination value of each feature point is obtained, wherein the first defect discrimination value is positively correlated with the difference and the discreteness, and the first defect discrimination value is negatively correlated with the grayscale value of the corresponding feature point.

[0050] It should be noted that the difference between two variables can be obtained by forward mapping of subtraction or division; the degree of dispersion between multiple variables can be obtained by calculating the extreme value, variance, etc. As an embodiment, the first defect discrimination value of the i-th feature point is recorded as K i , its formula form is: in, represents the mean grayscale value of all pixels except the central pixel in the 11×11 neighborhood centered on the i-th feature point; Qi Represents the gray value of the i-th feature point; d i Represents a sequence of grayscale values ​​of all pixels in the row where the i-th feature point is located; R() represents the range function; γ is a parameter adjustment factor preset to be greater than zero to prevent the denominator from being 0, with a value of 0.01; implementers can adjust it at their own discretion.

[0051] It should be understood that when When the value of is large, it means that the gray value of the i-th feature point is significantly different from the gray mean of the pixels in its 11×11 neighborhood. i ) is large, indicating that the grayscale values ​​of all pixels in the row where the i-th feature point is located are more discrete. i When the value of is small, it means that the gray value of the i-th feature point is small. In summary, the possibility that the i-th feature point has a small hole defect feature is greater.

[0052] The third step: according to the change characteristics of the grayscale values ​​between adjacent pixels in each row of the optical cable image to be inspected, the grayscale fluctuation value of each row is obtained; according to the difference between the grayscale fluctuation values ​​between each row and the adjacent rows, the defect judgment value of each row is obtained; according to the grayscale fluctuation value and defect judgment value of the row where each feature point is located, combined with the grayscale run-length matrix of the optical cable image to be inspected, the second defect characteristic value of each feature point is obtained.

[0053] For cable necking defects, the cable diameter at the necking point is smaller than that at the non-necking point. Therefore, the distance between the two pixels at the interface between the cable and the background in the column where the necking feature point is located is closer. The surface color of the cable at the necking point appears as black and white vertical stripes. Therefore, the grayscale value sequence of each row of pixels in the row where the necking feature point is located and in adjacent rows will have more data fluctuations of similar frequencies. Based on this, the grayscale fluctuation value of each row of the image to be tested is obtained:

[0054] For each row of the optical cable image to be inspected, a first-order difference sequence of each row is obtained based on the grayscale value, the difference between the number of positive numbers and the number of negative numbers in the first-order difference sequence is counted, and the grayscale fluctuation value of each row is obtained by combining the total number of positive numbers and negative numbers in the first-order difference sequence.

[0055] It should be noted that the statistical difference between the number of positive numbers and the number of negative numbers in the first-order difference sequence refers to the absolute value of the difference between the number of positive numbers and the number of negative numbers in the first-order difference sequence; the grayscale fluctuation value of each row is specifically the ratio of the total number to the absolute value of the difference; wherein, in order to avoid the situation where the denominator is zero, it is necessary to add a preset value greater than zero to the denominator, and the value is 0.01.

[0056] Furthermore, the defect judgment value of each row is obtained:

[0057] The defect judgment value of each row is obtained according to the fusion result of the difference in grayscale fluctuation value between each row and the adjacent rows of the optical cable image to be inspected.

[0058] As an example, taking row i as an example, the absolute difference between the grayscale fluctuation values ​​of row x, row x-1, and row x+1 is calculated. The reciprocal of the sum of the two absolute differences is used as the defect judgment value for each row. To avoid the denominator being zero, a preset value greater than zero, set to 0.01, is added to the denominator. For the first and last rows of the image, only the adjacent rows are calculated.

[0059] It should be understood that when a necking feature appears in an optical cable, multiple values ​​greater than 0 and less than 0 will appear in the first-order difference sequence of the sequence composed of the grayscale values ​​of each row of pixels in the corresponding row and adjacent rows where the optical cable necking feature appears, and the values ​​greater than 0 and less than 0 will appear alternately.

[0060] At the constriction of an optical cable, fine vertical streaks may be present, each of varying lengths. A vertical scan of the optical cable image is performed to obtain a grayscale runlength matrix for the image to be inspected. Several runs of varying lengths will appear in the grayscale levels of the feature points located in the constriction defect area. The calculation of the grayscale runlength matrix is ​​well known, and the detailed process is omitted here.

[0061] As an embodiment, taking the i-th feature point as an example, the second defect feature value of the i-th feature point is recorded as H i , its formula form is: Among them, D i Indicates the defect judgment value of the row where the i-th feature point is located; Indicates the number of non-zero values ​​in the row corresponding to the gray level of the i-th feature point in the gray run matrix; Δ i Represents the contrast value of the i-th feature point.

[0062] Among them, two feature points with the same ordinate as the i-th feature point and the largest direction value are obtained; the absolute value of the difference between the abscissas of the two feature points in the optical cable image to be detected is used as the comparison value of each feature point.

[0063] It should be understood that when D i When the value of is large, it means that the gray value sequence of all pixels in the row where the i-th feature point is located and the adjacent rows has more frequent data fluctuations; when When the value of is large, it means that the gray level of the i-th feature point has multiple runs of different lengths; when Δ i When the value of is small, it means that the diameter of the optical cable in the column where the i-th feature point is located is small, and the possibility that the i-th feature point is in the necking defect area is greater.

[0064] The fourth step: according to the first defect characteristic value and the second defect characteristic value, combined with the defect characteristics in the historical optical cable image, the defect thresholds corresponding to the various defect characteristic values ​​are obtained, and the defect identification is performed on the optical cable image to be inspected.

[0065] A preset number of historical optical cable defect images are obtained, wherein the historical optical cable defect images include at least one of a pinhole defect and a necking defect; for any defect feature value, threshold segmentation is performed on the any defect feature value of all feature points of each optical cable defect image to obtain a segmentation threshold of the any defect feature value of each optical cable defect image; a defect threshold of the any defect feature value of the optical cable defect image to be detected is obtained based on an average level of the segmentation thresholds of all the any defect feature values ​​of all optical cable defect images; the defect threshold includes a first defect threshold and a second defect threshold.

[0066] As an embodiment, the preset number is 600, and the implementer can adjust it according to actual conditions; the method for threshold segmentation is Otsu threshold segmentation; the average level of multiple variables can be measured by the average value of multiple variables, that is, the first defect threshold of the optical cable image to be detected is specifically the average value of the first defect feature values ​​corresponding to the segmentation threshold of all optical cable defect images; the second defect threshold of the optical cable image to be detected is specifically the average value of the second defect feature values ​​corresponding to the segmentation threshold of all optical cable defect images.

[0067] Among them, the defect threshold acquisition flow chart is as follows: Figure 2 shown.

[0068] When the mean value of all the first defect feature values ​​in the image of the optical cable to be detected is greater than the first defect threshold, and the mean value of all the second defect feature values ​​is greater than the second defect threshold, the defect identification result of the optical cable to be detected is the presence of a pinhole defect and a necking defect; when the mean value of all the first defect feature values ​​in the image of the optical cable to be detected is greater than the first defect threshold, and the mean value of all the second defect feature values ​​is less than or equal to the second defect threshold, the defect identification result of the optical cable to be detected is a pinhole defect; when the mean value of all the first defect feature values ​​in the image of the optical cable to be detected is less than or equal to the first defect threshold, and the mean value of all the second defect feature values ​​is greater than the second defect threshold, the defect identification result of the optical cable to be detected is a necking defect; otherwise, the defect identification result of the optical cable to be detected is the absence of these two defects.

[0069] Based on the same inventive concept as the above method, an embodiment of the present application also provides an ADSS optical cable defect automatic identification device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned ADSS optical cable defect automatic identification methods are implemented.

[0070] In summary, the embodiment of the present application first obtains the image of the optical cable to be inspected, and performs feature point detection on it. According to the grayscale difference between the feature point and the nearby pixel points, and the discrete characteristics of the grayscale values ​​of the nearby pixel points, a first characteristic defect value is constructed to characterize the characteristics of the small hole defect in the optical cable image. Its beneficial effect is that for each feature point in the optical cable image to be inspected, the possibility of it falling into the small hole defect area is analyzed, thereby improving the accuracy of corresponding defect recognition; further, the grayscale change analysis is performed on each row in the optical cable image to be inspected, and the difference in grayscale changes between adjacent rows is performed to obtain the grayscale fluctuation value and defect judgment value of each row. Its beneficial effect is that through the grayscale fluctuation value, it is judged whether there is a necking defect in the optical cable image to be inspected based on the change of texture features, and further through the defect judgment The texture features of each row are compared by the break value. If the texture fluctuation features are obvious and have similar fluctuation features, it can be preliminarily judged that there is a defect at the position corresponding to the feature point; the grayscale fluctuation value and the defect judgment value are combined with the grayscale run matrix to construct the second defect feature value. Its beneficial effect is that it comprehensively considers the texture features and shape features of the necking defect in the optical cable, and more accurately describes the defect features, so that the defects of the optical cable to be inspected are more accurately identified; the two defect features of the historical optical cable defect image are analyzed. Its beneficial effect is that different defect thresholds are obtained for different defects, which solves the problem that the same threshold cannot effectively distinguish these different types of defects, so that the defect identification results of the optical cable image to be inspected are more accurate and the efficiency of defect identification is higher.

[0071] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to the embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified function or action, or may be implemented by a combination of dedicated hardware and computer instructions.

[0072] It is obvious to those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and that the present application can be implemented in other specific forms without departing from the basic features of the present application. Therefore, from any point of view, the above embodiments of the present application should be regarded as exemplary and non-restrictive; modifications to the technical solutions described in the above embodiments, or equivalent replacement of some of the technical features therein, do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present application, and should be included in the scope of protection of the present application.

Claims

1. A method for automatically identifying defects in ADSS optical cables, characterized in that: The method comprises the following steps: Acquire an image of the optical cable to be inspected, wherein the optical cable to be inspected is placed horizontally, and its axis is parallel to both the upper and lower edge lines of the image of the optical cable to be inspected; Perform feature point detection on the optical cable image to be inspected, and obtain the first defect feature value of each feature point based on the difference in grayscale value between each feature point and the pixel points in the local range, combined with the discrete features of the grayscale values ​​of the pixel points in the local range; Based on the grayscale value variation characteristics between adjacent pixels in each row of the optical cable image to be inspected, the grayscale fluctuation value of each row is obtained; based on the difference between the grayscale fluctuation values ​​of each row and its adjacent rows, the defect judgment value of each row is obtained; based on the grayscale fluctuation value and defect judgment value of the row where each feature point is located, combined with the grayscale run-length matrix of the optical cable image to be inspected, the second defect feature value of each feature point is obtained; According to the first defect characteristic value and the second defect characteristic value, combined with the defect characteristics in the historical optical cable image, defect thresholds corresponding to various defect characteristic values ​​are obtained, and defects are identified on the optical cable image to be inspected.

2. The ADSS optical cable defect automatic identification method according to claim 1, characterized in that: The first defect characteristic value of each characteristic point is obtained as follows: The first defect discrimination value of each feature point is obtained based on the overall grayscale difference between each feature point and the pixels in its neighborhood, combined with the discrete degree of the grayscale values ​​of the pixels in the row where each feature point is located; The first defect discrimination value is positively correlated with the overall grayscale difference and the discrete degree; the first defect discrimination value is negatively correlated with the grayscale value of the corresponding feature point.

3. The ADSS optical cable defect automatic identification method according to claim 1, characterized in that: The grayscale fluctuation value of each row is obtained as follows: For each row of the optical cable image to be inspected, a first-order difference sequence of each row is obtained based on the grayscale value, and the difference between the number of positive numbers and the number of negative numbers in the first-order difference sequence is counted. Combined with the number of non-zero elements in the first-order difference sequence, the grayscale fluctuation value of each row is obtained.

4. The ADSS optical cable defect automatic identification method according to claim 1, characterized in that: The defect judgment value of each row is obtained as follows: According to the fusion result of the difference in grayscale fluctuation value between each row and adjacent rows of the optical cable image to be inspected, the defect judgment value of each row is obtained; wherein the defect judgment value is negatively correlated with the fusion result.

5. The ADSS optical cable defect automatic identification method according to claim 1, characterized in that: The process of obtaining the second defect characteristic value of each characteristic point includes: Obtain the direction value of each feature point during the feature point detection process; obtain the contrast value of each feature point based on the position characteristics of the direction value of each feature point in the vertical direction; Scan the optical cable image to be inspected in a vertical direction to obtain a grayscale run-length matrix of the optical cable image to be inspected; The second defect characteristic value of the i-th characteristic point is recorded as H i , its formula form is: Among them, D i Indicates the defect judgment value of the row where the i-th feature point is located; Indicates the number of non-zero values ​​in the row corresponding to the gray level of the i-th feature point in the gray run matrix; Δ i Represents the contrast value of the i-th feature point.

6. The ADSS optical cable defect automatic identification method according to claim 5, characterized in that: The specific process of obtaining the contrast value of each feature point is as follows: Two feature points with the same ordinate as each feature point and the largest direction value are obtained, and the absolute value of the difference between the abscissas of the two feature points in the optical cable image to be detected is used as the comparison value of each feature point.

7. The ADSS optical cable defect automatic identification method according to claim 1, characterized in that: The steps of obtaining the defect thresholds corresponding to various defect characteristic values ​​are: Obtain a preset number of historical optical cable defect images; According to the distribution of various defect feature values ​​in each historical optical cable defect image, a segmentation threshold value corresponding to the defect feature value of each historical optical cable defect image is obtained; According to the average level of the segmentation thresholds of various defect feature values ​​of all optical cable defect images, defect thresholds of various defect feature values ​​of the optical cable defect image to be detected are obtained; wherein the defect thresholds include a first defect threshold and a second defect threshold.

8. The ADSS optical cable defect automatic identification method according to claim 7, characterized in that: The segmentation threshold for obtaining the defect feature value corresponding to each historical optical cable defect image is specifically: For any defect feature value, threshold segmentation is performed on the any defect feature value of all feature points of each optical cable defect image to obtain a segmentation threshold of the any defect feature value of each optical cable defect image.

9. The ADSS optical cable defect automatic identification method according to claim 7, characterized in that: The process of identifying defects in the optical cable image to be inspected includes: When the average value of all the first defect feature values ​​in the image of the optical cable to be inspected is greater than the first defect threshold, and the average value of all the second defect feature values ​​is greater than the second defect threshold, the defect identification result of the optical cable to be inspected is that a pinhole defect and a necking defect exist; When the average value of all the first defect feature values ​​in the image of the optical cable to be inspected is greater than the first defect threshold, and the average value of all the second defect feature values ​​is less than or equal to the second defect threshold, the defect identification result of the optical cable to be inspected is a pinhole defect; When the mean value of all the first defect feature values ​​in the image of the optical cable to be inspected is less than or equal to the first defect threshold, and the mean value of all the second defect feature values ​​is greater than the second defect threshold, the defect identification result of the optical cable to be inspected is a necking defect; otherwise, the defect identification result of the optical cable to be inspected is that neither of these two defects exists.

10. An ADSS optical cable defect automatic identification device, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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