Cable production quality detection method and system

Abstract

The invention discloses a cable production quality detection method and system, and the method comprises the steps: obtaining a cable insulation layer surface image with qualified production quality, generating an image fingerprint, and storing the image fingerprint in a qualified cable database; after insulation injection molding of the current cable is completed, shooting an image of a cable insulation layer once every s second through three camera devices surrounding the cable; performing image compression and graying processing on the three shot cable insulation layer images at different angles, then performing discrete cosine Fourier transform, generating a transformation coefficient matrix, and then taking out a sub-matrix as a low-frequency domain matrix; generating a binary sequence through the low-frequency domain matrix and converting a hexadecimal character string; image fingerprints are obtained from a qualified cable database, the Hamming distance is calculated through the image fingerprints and hexadecimal character strings, and whether surface defects of the insulating layer occur or not is judged. According to the method, automatic quality monitoring in the cable production process is achieved, high detection accuracy is achieved under the condition that the detection speed is guaranteed, and the calculation process is simple.

Description

Technical Field

[0001] The present invention relates to the field of cable production quality detection, and in particular to a cable production quality detection method and system. Background Art

[0002] With the development of society, cables have become an indispensable industrial product. The social demand is constantly increasing, and the manufacturers and production scale are also expanding. The characteristic of cables is that they can be energized inside but insulated outside. They are composed of a rope-like structure, which is manifested as one, several or several groups of wires. These wires are wrapped with a layer of highly insulating covering material to protect the wires and provide electrical isolation. In the production process of cables, it is particularly important to test the production quality of the wire insulation layer, which is related to the overall production quality of the cable and the safety of electricity use.

[0003] However, most of the existing cable production quality inspections are carried out manually, and there is no automated quality monitoring during the production process. On the other hand, most inspection tasks in industrial production have high requirements on speed, and the prerequisite is not to reduce production efficiency. The existing machine learning models and deep learning models are usually large in scale, slow in detection speed and difficult to train. How to improve the detection speed while ensuring the detection accuracy is an urgent problem to be solved. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a cable production quality inspection method and system, aiming to solve the problem that the existing cable production quality inspection is mostly carried out by manual sampling, and automated quality monitoring during the production process is not realized. In addition, the existing machine learning models and deep learning models are usually large in scale, slow in detection speed and difficult to train, making it difficult to improve the detection speed while ensuring detection accuracy.

[0005] In view of the above problems, the present application provides a cable production quality detection method and system.

[0006] The first aspect disclosed in the present application provides a method for detecting the quality of cable production, the method comprising the following steps: Step 1: Obtain a surface image of the insulation layer of a cable with qualified production quality, generate an image fingerprint of the surface image of the insulation layer of a cable with qualified production quality, and store it in a qualified cable database; Step 2: After the insulation injection molding of the current cable is completed, an image of the cable insulation layer is captured every s seconds by three cameras surrounding the cable, wherein the three cameras surrounding the cable are equally spaced 120° apart, and s is less than the ratio of the cable moving distance per second on the production line to the width of the camera shooting range; Step 3: The cable insulation layer images at three different angles taken by three cameras surrounding the cable are compressed and grayscaled to generate three compressed grayscale images with a resolution of 32×32 pixels; Step 4: Perform discrete cosine Fourier transform on the three compressed grayscale images with a resolution of 32×32 pixels, generate three transformation coefficient matrices with a dimension of 32×32, and take out sub-matrices with a dimension of 8×8 from the upper left corners of the three transformation coefficient matrices with a dimension of 32×32, to generate three low-frequency domain matrices; Step 5: For each low-frequency domain matrix, calculate the corresponding matrix average value, and compare each element of the low-frequency domain matrix with the corresponding matrix average value. If it is greater than or equal to the low-frequency domain matrix average value, it is recorded as 1, and if it is less than the low-frequency domain matrix average value, it is recorded as 0. Generate three 64-bit binary sequences, and then convert the three 64-bit binary sequences into hexadecimal respectively to generate three strings of length 16; Step 6: Obtain the image fingerprint of the current cable from the qualified cable database, calculate the Hamming distance with three strings of length 16 in turn and sum them up. If the sum is greater than the judgment threshold, it is judged that there is a suspected surface defect of the insulation layer.

[0007] Preferably, the step 1 specifically includes the following steps: Step 1.1: Obtain a surface image of a cable insulation layer with qualified production quality, perform image compression and grayscale processing, and generate a compressed grayscale standard image with a resolution of 32×32 pixels for the surface image of the cable insulation layer with qualified production quality; Step 1.2: Perform discrete cosine Fourier transform on the compressed grayscale standard image generated in step 1.1 to generate a standard transformation coefficient matrix with a dimension of 32×32 for producing a cable insulation layer surface image with qualified production quality, and take out a sub-matrix with a dimension of 8×8 from the upper left corner of the standard transformation coefficient matrix to generate a standard low-frequency domain matrix; Step 1.3: Calculate the matrix average value of the standard low-frequency domain matrix, and compare each element of the standard low-frequency domain matrix with the matrix average value. If it is greater than or equal to the matrix average value, it is recorded as 1, and if it is less than the matrix average value, it is recorded as 0, to generate a 64-bit binary standard sequence, and then convert the 64-bit binary standard sequence into hexadecimal to generate a standard string of length 16 as the image fingerprint of the surface image of the cable insulation layer of qualified production quality; Step 1.4: Using the production batch of the currently produced cables with qualified quality as the primary key, the image fingerprint of the surface image of the insulation layer of the cables with qualified production quality is stored in the qualified cable database in the form of a character string.

[0008] Preferably, the step 3 specifically includes the following steps: Step 3.1: Perform average pooling on the cable insulation layer images at three different angles taken by three cameras surrounding the cable to generate three compressed images with a resolution of 32×32 pixels; Step 3.2: The three compressed images with a resolution of 32×32 pixels are grayed out by weighted averaging method to generate three compressed gray images with a resolution of 32×32 pixels.

[0009] Preferably, the step 4 specifically includes the following steps: Step 4.1: Convert the three compressed grayscale images with a resolution of 32×32 pixels into a matrix, denoted as P x , x=1,2,3, used to represent the labels of three compressed grayscale images with a resolution of 32×32 pixels, P x The dimension is 32×32; Step 4.2: Through D x =AP x A T The three transform coefficient matrices with dimensions of 32×32 are calculated and generated in the following way, where D x is a transformation coefficient matrix with a dimension of 32×32, matrix A is a transformation matrix with a dimension of 32×32, , the value of N is the matrix A and the matrix P x The dimension value of is 32 here, i and j are the row and column labels of matrix A respectively; Step 4.3: Take out sub-matrices with a dimension of 8×8 from the upper left corners of the three transform coefficient matrices with a dimension of 32×32 respectively, and generate three low-frequency domain matrices.

[0010] Preferably, the step 6 specifically comprises the following steps: Step 6.1: Obtain the image fingerprint of the current cable from the qualified cable database; Step 6.2: If the length of the image fingerprint is greater than 16, the first 16 bits of the image fingerprint are retained to make the length of the image fingerprint equal to 16. If the length of the image fingerprint is less than 16, random characters are added to the end of the image fingerprint to make the length of the image fingerprint equal to 16. Step 6.3: Compare the image fingerprint with the three strings of length 16 one by one to find the number of characters that are different at the corresponding positions, generate three Hamming distance values, and sum the three Hamming distance values ​​to generate the total Hamming distance; Step 6.4: If the total Hamming distance is greater than the determination threshold, it is determined that the current cable is suspected of having insulation surface defects, wherein the determination threshold is set to 6, and insulation surface defects include surface bumps, cracks or bubbles.

[0011] The second aspect disclosed in the present application provides a cable production quality detection system, the system is used to implement the above-mentioned cable production quality detection method, the system includes: A database module, the database module is used to obtain the surface image of the insulation layer of the cable with qualified production quality, generate the image fingerprint of the surface image of the insulation layer of the cable with qualified production quality, and store it in the qualified cable database; A camera module, wherein after the insulation injection molding of the current type of cable is completed, the camera module takes an image of the cable insulation layer once every s seconds through three camera devices surrounding the cable, wherein the three camera devices surrounding the cable are equally spaced 120° apart, and s is less than the ratio of the cable moving distance per second on the production line to the width of the camera shooting range; A preprocessing module, the preprocessing module is used to compress and grayscale the images of the cable insulation layer at three different angles taken by three cameras surrounding the cable, and generate three compressed grayscale images with a resolution of 32×32 pixels; A transformation module, wherein the transformation module is used to perform discrete cosine Fourier transformation on three compressed grayscale images with a resolution of 32×32 pixels, respectively, to generate three transformation coefficient matrices with a dimension of 32×32, and to extract sub-matrices with a dimension of 8×8 from the upper left corners of the three transformation coefficient matrices with a dimension of 32×32, respectively, to generate three low-frequency domain matrices; A hash module is used to calculate the corresponding matrix average value for each low-frequency domain matrix, and compare each element of the low-frequency domain matrix with the corresponding matrix average value. If it is greater than or equal to the low-frequency domain matrix average value, it is recorded as 1, and if it is less than the low-frequency domain matrix average value, it is recorded as 0, generating three 64-bit binary sequences, and then converting the three 64-bit binary sequences into hexadecimal respectively to generate three strings of length 16; The detection module is used to obtain the image fingerprint of the current type of cable from the qualified cable database, calculate the Hamming distance with three strings of length 16 in turn and sum them up. If the sum is greater than the judgment threshold, it is judged as a suspected surface defect of the insulation layer.

[0012] The beneficial effects of the present invention are: (1) It realizes the automated quality monitoring of the insulation layer during the cable production process, solving the problem that the existing cable production quality inspection is inefficient and prone to errors due to manual sampling.

[0013] (2) It does not use existing large-scale machine learning models or deep learning models, and does not require large data sets for training. It achieves high detection accuracy while ensuring detection speed without reducing production efficiency.

[0014] (3) The calculation process is simple, requiring only a few matrix transformations and string comparisons to complete the detection task without consuming too much computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 The present invention is an overall flow chart of a cable production quality inspection method.

[0017] Figure 2 This is the overall structure diagram of a cable production quality inspection system. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, an embodiment of the present application provides a cable production quality detection method, the method comprising the following steps: Step 1: Obtain a surface image of the insulation layer of a cable with qualified production quality, generate an image fingerprint of the surface image of the insulation layer of a cable with qualified production quality, and store it in a qualified cable database; Step 2: After the insulation injection molding of the current cable is completed, an image of the cable insulation layer is captured every s seconds by three cameras surrounding the cable, wherein the three cameras surrounding the cable are equally spaced 120° apart, and s is less than the ratio of the cable moving distance per second on the production line to the width of the camera shooting range; Step 3: The cable insulation layer images at three different angles taken by three cameras surrounding the cable are compressed and grayscaled to generate three compressed grayscale images with a resolution of 32×32 pixels; Step 4: Perform discrete cosine Fourier transform on the three compressed grayscale images with a resolution of 32×32 pixels, generate three transformation coefficient matrices with a dimension of 32×32, and take out sub-matrices with a dimension of 8×8 from the upper left corners of the three transformation coefficient matrices with a dimension of 32×32, to generate three low-frequency domain matrices; Step 5: For each low-frequency domain matrix, calculate the corresponding matrix average value, and compare each element of the low-frequency domain matrix with the corresponding matrix average value. If it is greater than or equal to the low-frequency domain matrix average value, it is recorded as 1, and if it is less than the low-frequency domain matrix average value, it is recorded as 0. Generate three 64-bit binary sequences, and then convert the three 64-bit binary sequences into hexadecimal respectively to generate three strings of length 16; Step 6: Obtain the image fingerprint of the current cable from the qualified cable database, calculate the Hamming distance with three strings of length 16 in turn and sum them up. If the sum is greater than the judgment threshold, it is judged that there is a suspected surface defect of the insulation layer.

[0020] Furthermore, the step 1 specifically includes the following steps: Step 1.1: Obtain a surface image of a cable insulation layer with qualified production quality, perform image compression and grayscale processing, and generate a compressed grayscale standard image with a resolution of 32×32 pixels for the surface image of the cable insulation layer with qualified production quality; Step 1.2: Perform discrete cosine Fourier transform on the compressed grayscale standard image generated in step 1.1 to generate a standard transformation coefficient matrix with a dimension of 32×32 for producing a cable insulation layer surface image with qualified production quality, and take out a sub-matrix with a dimension of 8×8 from the upper left corner of the standard transformation coefficient matrix to generate a standard low-frequency domain matrix; Step 1.3: Calculate the matrix average value of the standard low-frequency domain matrix, and compare each element of the standard low-frequency domain matrix with the matrix average value. If it is greater than or equal to the matrix average value, it is recorded as 1, and if it is less than the matrix average value, it is recorded as 0, to generate a 64-bit binary standard sequence, and then convert the 64-bit binary standard sequence into hexadecimal to generate a standard string of length 16 as the image fingerprint of the surface image of the cable insulation layer of qualified production quality; Step 1.4: Using the production batch of the currently produced cables with qualified quality as the primary key, the image fingerprint of the surface image of the insulation layer of the cables with qualified production quality is stored in the qualified cable database in the form of a character string.

[0021] Specifically, image fingerprints can characterize the features of the surface image of the cable insulation layer of production quality qualified and project the image information into the semantic space.

[0022] Furthermore, the step 3 specifically includes the following steps: Step 3.1: Perform average pooling on the cable insulation layer images at three different angles taken by three cameras surrounding the cable to generate three compressed images with a resolution of 32×32 pixels; Step 3.2: The three compressed images with a resolution of 32×32 pixels are grayed out by weighted averaging method to generate three compressed gray images with a resolution of 32×32 pixels.

[0023] Specifically, the steps of performing image compression and grayscale processing on the image can simplify unnecessary calculations and improve the convenience and calculation speed of the method.

[0024] Furthermore, the step 4 specifically includes the following steps: Step 4.1: Convert the three compressed grayscale images with a resolution of 32×32 pixels into a matrix, denoted as P x , x=1,2,3, used to represent the labels of three compressed grayscale images with a resolution of 32×32 pixels, P x The dimension is 32×32; Step 4.2: Through D x =AP x A T The three transform coefficient matrices with dimensions of 32×32 are calculated and generated in the following way, where D x is a transformation coefficient matrix with a dimension of 32×32, matrix A is a transformation matrix with a dimension of 32×32, , the value of N is the matrix A and the matrix P x The dimension value of is 32 here, i and j are the row and column labels of matrix A respectively; Step 4.3: Take out sub-matrices with a dimension of 8×8 from the upper left corners of the three transform coefficient matrices with a dimension of 32×32 respectively, and generate three low-frequency domain matrices.

[0025] Specifically, the discrete cosine Fourier transform converts the image from the pixel-level spatial domain to the frequency domain, and obtains a transform coefficient matrix with a dimension of 32×32 to capture the low-frequency information of the image. After the discrete cosine Fourier transform, the frequency characteristics of the image are concentrated in the upper left corner of the transform coefficient matrix, and the sub-matrix with a dimension of 8×8 in the upper left corner of the transform coefficient matrix is ​​usually retained because the sub-matrix with a dimension of 8×8 in the upper left corner of the transform coefficient matrix presents the lowest frequency in the image.

[0026] Furthermore, the step 6 specifically includes the following steps: Step 6.1: Obtain the image fingerprint of the current cable from the qualified cable database; Step 6.2: If the length of the image fingerprint is greater than 16, the first 16 bits of the image fingerprint are retained to make the length of the image fingerprint equal to 16. If the length of the image fingerprint is less than 16, random characters are added to the end of the image fingerprint to make the length of the image fingerprint equal to 16. Step 6.3: Compare the image fingerprint with the three strings of length 16 one by one to find the number of characters that are different at the corresponding positions, generate three Hamming distance values, and sum the three Hamming distance values ​​to generate the total Hamming distance; Step 6.4: If the total Hamming distance is greater than the determination threshold, it is determined that the current cable is suspected of having insulation surface defects, wherein the determination threshold is set to 6, and insulation surface defects include surface bumps, cracks or bubbles.

[0027] Specifically, by using the Hamming distance to compare in the above manner, the similarity of the images can be evaluated. The smaller the Hamming distance, the more similar the images are.

[0028] In summary, the cable production quality detection method provided by the embodiment of the present application has the following technical effects: (1) It realizes the automated quality monitoring of the insulation layer during the cable production process, solving the problem that the existing cable production quality inspection is inefficient and prone to errors due to manual sampling.

[0029] (2) It does not use existing large-scale machine learning models or deep learning models, and does not require large data sets for training. It achieves high detection accuracy while ensuring detection speed without reducing production efficiency.

[0030] (3) The calculation process is simple, requiring only a few matrix transformations and string comparisons to complete the detection task without consuming too much computing resources.

[0031] Based on the same inventive concept as the cable production quality detection method in the aforementioned embodiment, Figure 2 As shown, the present application provides a cable production quality detection system, the system comprising: A database module, the database module is used to obtain the surface image of the insulation layer of the cable with qualified production quality, generate the image fingerprint of the surface image of the insulation layer of the cable with qualified production quality, and store it in the qualified cable database; A camera module, wherein after the insulation injection molding of the current type of cable is completed, the camera module takes an image of the cable insulation layer once every s seconds through three camera devices surrounding the cable, wherein the three camera devices surrounding the cable are equally spaced 120° apart, and s is less than the ratio of the cable moving distance per second on the production line to the width of the camera shooting range; A preprocessing module, the preprocessing module is used to compress and grayscale the images of the cable insulation layer at three different angles taken by three cameras surrounding the cable, and generate three compressed grayscale images with a resolution of 32×32 pixels; A transformation module, wherein the transformation module is used to perform discrete cosine Fourier transformation on three compressed grayscale images with a resolution of 32×32 pixels, respectively, to generate three transformation coefficient matrices with a dimension of 32×32, and to extract sub-matrices with a dimension of 8×8 from the upper left corners of the three transformation coefficient matrices with a dimension of 32×32, respectively, to generate three low-frequency domain matrices; A hash module is used to calculate the corresponding matrix average value for each low-frequency domain matrix, and compare each element of the low-frequency domain matrix with the corresponding matrix average value. If it is greater than or equal to the low-frequency domain matrix average value, it is recorded as 1, and if it is less than the low-frequency domain matrix average value, it is recorded as 0, generating three 64-bit binary sequences, and then converting the three 64-bit binary sequences into hexadecimal respectively to generate three strings of length 16; The detection module is used to obtain the image fingerprint of the current type of cable from the qualified cable database, calculate the Hamming distance with three strings of length 16 in turn and sum them up. If the sum is greater than the judgment threshold, it is judged as a suspected surface defect of the insulation layer.

[0032] Through the above-mentioned detailed description of a cable production quality inspection method in this specification, those skilled in the art can clearly understand a cable production quality inspection system in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0033] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0034] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A cable production quality detection method, characterized in that: The method comprises the following steps: Step 1: Obtain a surface image of the insulation layer of a cable with qualified production quality, generate an image fingerprint of the surface image of the insulation layer of a cable with qualified production quality, and store it in a qualified cable database; Step 2: After the insulation injection molding of the current cable is completed, an image of the cable insulation layer is captured every s seconds by three cameras surrounding the cable, wherein the three cameras surrounding the cable are equally spaced 120° apart, and s is less than the ratio of the cable moving distance per second on the production line to the width of the camera shooting range; Step 3: The cable insulation layer images at three different angles taken by three cameras surrounding the cable are compressed and grayscaled to generate three compressed grayscale images with a resolution of 32×32 pixels; Step 4: Perform discrete cosine Fourier transform on the three compressed grayscale images with a resolution of 32×32 pixels, generate three transformation coefficient matrices with a dimension of 32×32, and take out sub-matrices with a dimension of 8×8 from the upper left corners of the three transformation coefficient matrices with a dimension of 32×32, to generate three low-frequency domain matrices; Step 5: For each low-frequency domain matrix, calculate the corresponding matrix average value, and compare each element of the low-frequency domain matrix with the corresponding matrix average value. If it is greater than or equal to the low-frequency domain matrix average value, it is recorded as 1, and if it is less than the low-frequency domain matrix average value, it is recorded as 0. Generate three 64-bit binary sequences, and then convert the three 64-bit binary sequences into hexadecimal respectively to generate three strings of length 16; Step 6: Obtain the image fingerprint of the current cable from the qualified cable database, calculate the Hamming distance with three strings of length 16 in turn and sum them up. If the sum is greater than the judgment threshold, it is judged that there is a suspected surface defect of the insulation layer.

2. A cable production quality detection method according to claim 1, characterized in that: The step 1 specifically comprises the following steps: Step 1.1: Obtain a surface image of a cable insulation layer with qualified production quality, perform image compression and grayscale processing, and generate a compressed grayscale standard image with a resolution of 32×32 pixels for the surface image of the cable insulation layer with qualified production quality; Step 1.2: Perform discrete cosine Fourier transform on the compressed grayscale standard image generated in step 1.1 to generate a standard transformation coefficient matrix with a dimension of 32×32 for producing a cable insulation layer surface image with qualified production quality, and take out a sub-matrix with a dimension of 8×8 from the upper left corner of the standard transformation coefficient matrix to generate a standard low-frequency domain matrix; Step 1.3: Calculate the matrix average value of the standard low-frequency domain matrix, and compare each element of the standard low-frequency domain matrix with the matrix average value. If it is greater than or equal to the matrix average value, it is recorded as 1, and if it is less than the matrix average value, it is recorded as 0, to generate a 64-bit binary standard sequence, and then convert the 64-bit binary standard sequence into hexadecimal to generate a standard string of length 16 as the image fingerprint of the surface image of the cable insulation layer of qualified production quality; Step 1.4: Using the production batch of the currently produced cables with qualified quality as the primary key, the image fingerprint of the surface image of the insulation layer of the cables with qualified production quality is stored in the qualified cable database in the form of a character string.

3. A cable production quality detection method according to claim 1, characterized in that: The step 3 specifically comprises the following steps: Step 3.1: Perform average pooling on the cable insulation layer images at three different angles taken by three cameras surrounding the cable to generate three compressed images with a resolution of 32×32 pixels; Step 3.2: The three compressed images with a resolution of 32×32 pixels are grayed out by weighted averaging method to generate three compressed gray images with a resolution of 32×32 pixels.

4. A cable production quality detection method according to claim 1, characterized in that: The step 4 specifically comprises the following steps: Step 4.1: Convert the three compressed grayscale images with a resolution of 32×32 pixels into a matrix, denoted as P x , x=1,2,3, used to represent the labels of three compressed grayscale images with a resolution of 32×32 pixels, P x The dimension is 32×32; Step 4.2: Through D x =AP x A T The three transform coefficient matrices with dimensions of 32×32 are calculated and generated in the following way, where D x is a transformation coefficient matrix with a dimension of 32×32, matrix A is a transformation matrix with a dimension of 32×32, , the value of N is the matrix A and the matrix P x The dimension value of is 32 here, i and j are the row and column labels of matrix A respectively; Step 4.3: Take out sub-matrices with a dimension of 8×8 from the upper left corners of the three transform coefficient matrices with a dimension of 32×32 respectively, and generate three low-frequency domain matrices.

5. A cable production quality detection method according to claim 1, characterized in that: The step 6 specifically comprises the following steps: Step 6.1: Obtain the image fingerprint of the current cable from the qualified cable database; Step 6.2: If the length of the image fingerprint is greater than 16, the first 16 bits of the image fingerprint are retained to make the length of the image fingerprint equal to 16. If the length of the image fingerprint is less than 16, random characters are added to the end of the image fingerprint to make the length of the image fingerprint equal to 16. Step 6.3: Compare the image fingerprint with the three strings of length 16 one by one to find the number of characters that are different at the corresponding positions, generate three Hamming distance values, and sum the three Hamming distance values ​​to generate the total Hamming distance; Step 6.4: If the total Hamming distance is greater than the determination threshold, it is determined that the current cable is suspected of having insulation surface defects, wherein the determination threshold is set to 6, and insulation surface defects include surface bumps, cracks or bubbles.

6. A cable production quality detection system, the system comprising: A database module, the database module is used to obtain the surface image of the insulation layer of the cable with qualified production quality, generate the image fingerprint of the surface image of the insulation layer of the cable with qualified production quality, and store it in the qualified cable database; A camera module, wherein after the insulation injection molding of the current type of cable is completed, the camera module takes an image of the cable insulation layer once every s seconds through three camera devices surrounding the cable, wherein the three camera devices surrounding the cable are equally spaced 120° apart, and s is less than the ratio of the cable moving distance per second on the production line to the width of the camera shooting range; A preprocessing module, the preprocessing module is used to compress and grayscale the images of the cable insulation layer at three different angles taken by three cameras surrounding the cable, and generate three compressed grayscale images with a resolution of 32×32 pixels; A transformation module, wherein the transformation module is used to perform discrete cosine Fourier transformation on three compressed grayscale images with a resolution of 32×32 pixels, respectively, to generate three transformation coefficient matrices with a dimension of 32×32, and to extract sub-matrices with a dimension of 8×8 from the upper left corners of the three transformation coefficient matrices with a dimension of 32×32, respectively, to generate three low-frequency domain matrices; A hash module is used to calculate the corresponding matrix average value for each low-frequency domain matrix, and compare each element of the low-frequency domain matrix with the corresponding matrix average value. If it is greater than or equal to the low-frequency domain matrix average value, it is recorded as 1, and if it is less than the low-frequency domain matrix average value, it is recorded as 0, generating three 64-bit binary sequences, and then converting the three 64-bit binary sequences into hexadecimal respectively to generate three strings of length 16; The detection module is used to obtain the image fingerprint of the current type of cable from the qualified cable database, calculate the Hamming distance with three strings of length 16 in turn and sum them up. If the sum is greater than the judgment threshold, it is judged as a suspected surface defect of the insulation layer.

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