Cable delivery detection system based on image recognition technology

Abstract

The invention discloses a cable delivery detection system based on an image recognition technology. The cable delivery detection system comprises a light source, an optical medium marking module, an electromagnetic excitation module, a load module, an optical imaging module, a defect analysis module and a central processing unit. The light source is arranged along the axial direction of the cable, comprises multiple groups of sub-light sources, and irradiates the surface of the cable at a preset incident angle by adjusting light through the polaroid and the wavelength adjusting unit; the optical medium marking module is used for pasting a reflective marking layer containing a fluorescent layer and a thermochromic layer on the surface of the cable; the electromagnetic excitation module generates a high-frequency alternating electromagnetic field with a specific frequency in the cable; the load module applies a multi-axial bending load to the cable; the light imaging module collects light reflection data in a stroboscopic mode around the circumference of the cable. The defect analysis module determines cable defects according to the fluorescent layer deformation points. According to the system, multi-dimensional and high-precision detection is realized, the detection efficiency and accuracy are improved, the anti-interference capability is enhanced, and the cable delivery detection requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a cable factory detection system based on image recognition technology. Background Art

[0002] In the fields of power transmission and industrial applications, cables serve as important carriers of electrical energy, and their quality directly impacts the safe and stable operation of power systems. Traditional cable factory inspection techniques often rely on manual visual inspection, simple mechanical measurements, or single nondestructive testing methods. Manual visual inspection relies on the experience and subjective judgment of the inspector, resulting in low efficiency, susceptibility to human interference, and difficulty detecting minor defects. Mechanical measurement can only detect the cable's external dimensions and cannot effectively assess internal defects and material properties. Single nondestructive testing methods, such as ultrasonic testing and X-ray testing, while capable of detecting some internal defects, have limited ability to detect surface defects. Furthermore, test results are easily affected by the cable's structure and material properties, making it difficult to comprehensively and accurately detect all types of defects in the cable. With the advancement of cable manufacturing technology and the increasing demand for cable quality in various application scenarios, traditional inspection techniques can no longer meet the needs for high-precision, high-efficiency, and comprehensive inspection. A more advanced and reliable cable factory inspection technology is urgently needed. Summary of the Invention

[0003] In order to solve the above problems, the present invention provides a cable factory inspection system based on image recognition technology.

[0004] The purpose of the present invention is achieved by adopting the following technical solutions:

[0005] A cable factory inspection system based on image recognition technology, comprising:

[0006] A light source is configured to be arranged along the axial direction of the cable, and includes multiple groups of sub-light sources, each of which is provided with a corresponding rotatable polarizer and a wavelength adjustment unit, the wavelength adjustment unit being used to adjust the wavelength band of the light of the sub-light source, and each of the sub-light sources irradiates the outer surface of the cable at a preset incident angle range;

[0007] An optical medium marking module is configured to apply a reflective marking layer to the surface of the cable, wherein the reflective marking layer includes a fluorescent layer and a thermochromic layer, and the color threshold of the thermochromic layer is equal to the yield strength of the outer sheath material of the cable;

[0008] An electromagnetic excitation module is configured to generate a high-frequency alternating electromagnetic field inside the cable, wherein the frequency range of the high-frequency alternating electromagnetic field covers the resonant frequency interval of the conductor and the insulation layer of the cable;

[0009] a load module configured to be fixed to an end portion of the cable and to apply a multi-axial bending load to the cable;

[0010] an optical imaging module configured to surround the circumference of the cable at preset angular intervals and collect light reflection data from the cable surface by strobing, wherein the strobing frequency is configured to be an integer multiple of the electromagnetic excitation frequency of the electromagnetic excitation module;

[0011] The defect analysis module is configured to obtain light reflection data when receiving alternating excitation under different bending loads, and determine the defects of the cable based on the fluorescent deformation points and thermal deformation points of the fluorescent layer.

[0012] The central processing unit is configured to connect to each module to obtain signal responses and requests from each module.

[0013] As a preferred embodiment, a plurality of fluorescent deformation regions are arranged at intervals in the fluorescent layer, and one or more movable fluorescent particles are arranged in each of the fluorescent deformation regions.

[0014] As a preferred embodiment, the defect analysis module is configured to perform the following steps:

[0015] Step S1: Dynamic image sequence capture

[0016] During the electromagnetic excitation process, a deformation image sequence of the fluorescent deformation area is collected at a rate of more than 5000 frames per second;

[0017] Synchronously record the displacement and electromagnetic field intensity parameters of the bending loading module;

[0018] Step S2: Optical feature enhancement processing

[0019] Separate the polarization components of the original image and extract the polarization component image parallel to the normal direction of the cable surface;

[0020] Based on the thermochromic characteristics, the areas in the image that exceed the material yield strength are identified and marked as abnormal areas in the red channel;

[0021] Step S3: Deformation trajectory analysis

[0022] Three adjacent frames of images are selected to form an analysis unit, and the motion vector of the marker layer particles is calculated using a pixel displacement tracking algorithm;

[0023] Generate a displacement-time curve for each particle and screen out abnormal vibration points whose amplitude exceeds the safety threshold;

[0024] Step S4: Defect type identification

[0025] For surface crack defects: identify the discontinuous jump points appearing in the trajectory of the fluorescent particles and detect the extension length of the jump points along the cable axis;

[0026] For internal bubble defects: analyze the phase lag of particle vibration. When the phase difference of five consecutive analysis units in the same area exceeds 30%, it is determined to be an internal cavity.

[0027] For material fatigue defects: the geometric topology of the abnormal area in the red channel is extracted. If the area shows a dendritic fractal structure and the area accounts for more than 0.5%, it is determined to be material failure;

[0028] Step S5: 3D defect reconstruction

[0029] Fuse circumferential multi-camera view data to reconstruct a 3D point cloud model of the defect area;

[0030] Defect levels are divided according to the curvature distribution of the point cloud, and areas where the curvature mutation exceeds 2 times the baseline value are marked as high-risk defects.

[0031] As a preferred embodiment, a collaborative control module is further included, which is configured to:

[0032] Automatically adjust the illumination angle of the LED array and the rotation angle of the polarizer according to the cable diameter;

[0033] When a surface crack is detected, the bending loading module is triggered to increase the flexural load in the axial direction to verify the defect propagation;

[0034] Generate a digital inspection report including defect location, type and 3D model.

[0035] As a preferred embodiment, the pixel displacement tracking algorithm in step S3 further includes:

[0036] A 20 × 20 pixel tracking window was established for each particle;

[0037] A pyramid hierarchical search strategy is used between adjacent frames, first performing coarse matching with a step size of 5 pixels, and then performing fine matching with a step size of 1 pixel;

[0038] When the matching error exceeds the preset threshold, fluorescence spectrum feature-assisted tracking is enabled, and the tracking path is corrected by comparing the RGB histogram distribution of the particles.

[0039] As a preferred method, the phase difference calculation in step S4 adopts:

[0040] Perform Hilbert transform on the vibration signal to generate an analytical signal;

[0041] Calculate the cosine similarity of the analytical signals of adjacent analysis units, and trigger a phase difference alarm when the similarity is lower than 0.7;

[0042] Combined with the electromagnetic field intensity change curve, phase fluctuations caused by external interference are eliminated.

[0043] As a preferred embodiment, the marking medium module further comprises:

[0044] Before spraying, the cable surface is plasma cleaned to control the surface roughness Ra value within the range of 0.8-1.2μm;

[0045] An electrostatic adsorption process is used to distribute the nanoparticles in a stripe shape along the circumference of the cable, with the stripe spacing being equivalent to the minimum detectable defect size.

[0046] As a preferred embodiment, the optical imaging module dynamically adjusts the stroboscopic duty cycle according to the ambient light intensity during image acquisition, and activates the optical chopper in a strong light environment to eliminate ambient light interference;

[0047] The deconvolution restoration algorithm is used for motion blurred images, and a pre-calibrated point spread function is used to deblur the image.

[0048] The beneficial effects of the present invention are:

[0049] 1. Significantly improved multi-dimensional detection capabilities: The system integrates multiple functional modules, including a light source, an optical medium marking module, an electromagnetic excitation module, and a load module. The light source, through multiple sets of sub-light sources, a rotatable polarizer, and a wavelength adjustment unit, can illuminate the outer surface of the cable at a preset incident angle range. The optical imaging module collects light reflection data at multiple angles and high frequencies. The electromagnetic excitation module generates a high-frequency alternating electromagnetic field within a specific frequency range inside the cable, and the load module applies multi-axial bending loads, enabling multi-dimensional detection of surface and internal defects in the cable. Compared to traditional single detection methods, this system can more comprehensively detect defects in the cable under different conditions, greatly improving the accuracy and comprehensiveness of detection.

[0050] Defect detection accuracy has been significantly improved: The reflective marking layer set up by the optical medium marking module includes a fluorescent layer and a thermochromic layer. The fluorescent layer is spaced apart and contains multiple fluorescent deformation zones and movable fluorescent particles. Combined with a series of precise processing steps in the defect analysis module, such as high-speed image acquisition at more than 5,000 frames per second, polarization component separation, and pixel displacement tracking algorithms, it can accurately capture the tiny deformation and movement trajectory of the fluorescent particles. Different types of defects such as surface cracks, internal bubble defects, and material fatigue defects can be accurately identified and located through specific discrimination algorithms. At the same time, through three-dimensional defect reconstruction technology, a three-dimensional point cloud model of the defect area is generated and the defect level is divided, which brings the defect detection accuracy to a new level and effectively avoids the problems of missed detection and misjudgment caused by the lack of accuracy of traditional detection methods.

[0051] Significantly improved inspection efficiency: The optical imaging module performs stroboscopic data acquisition around the cable circumference at preset angle intervals, with the stroboscopic frequency being an integer multiple of the electromagnetic excitation frequency. This allows for rapid acquisition of light reflection data from the cable surface under varying conditions. The defect analysis module utilizes efficient image processing algorithms and automated defect identification procedures to rapidly analyze and process the collected data. Compared to traditional manual inspections and complex data analysis processes, this significantly shortens inspection time and improves efficiency, meeting the factory inspection requirements for large-scale cable production.

[0052] Enhanced adaptive detection capabilities: The collaborative control module can automatically adjust the LED array's illumination angle and polarizer rotation angle based on the cable diameter, enabling the system to adapt to the detection needs of cables of different specifications. When a surface crack is detected, the bending loading module is automatically triggered to increase the flexural load in that axis to verify the defect's extensibility, enabling intelligent and adaptive adjustment of the detection process. At the same time, a digital inspection report containing the defect location, type, and 3D model is generated to facilitate subsequent quality traceability and production improvements. Compared with traditional fixed parameter inspection methods, the system's versatility and practicality have been significantly improved.

[0053] Optimized anti-interference capabilities and image quality: The optical imaging module dynamically adjusts the strobe duty cycle based on ambient light intensity during image acquisition. In bright light environments, an optical chopper is activated to eliminate ambient light interference. A deconvolution restoration algorithm is used to deblur motion-blurred images, ensuring the clarity and accuracy of captured images. The marking medium module uses plasma cleaning and electrostatic adsorption processes to treat the cable surface, ensuring a better bond between the marking layer and the cable surface and improving the stability of the detection signal. These measures effectively enhance the system's anti-interference capabilities, ensuring high-quality detection data even in complex environments and improving the reliability of detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0055] Figure 1 It is a structural diagram of the system of embodiment 1 of the present invention. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0057] The present disclosure provides a cable factory inspection system based on image recognition technology. Figure 1 Shown, including:

[0058] A light source is configured to be arranged along the axial direction of the cable, and includes multiple groups of sub-light sources, each of which is provided with a corresponding rotatable polarizer and a wavelength adjustment unit, the wavelength adjustment unit being used to adjust the wavelength band of the light of the sub-light source, and each of the sub-light sources irradiates the outer surface of the cable at a preset incident angle range;

[0059] An optical medium marking module is configured to apply a reflective marking layer to the surface of the cable, wherein the reflective marking layer includes a fluorescent layer and a thermochromic layer, and the color threshold of the thermochromic layer is equal to the yield strength of the outer sheath material of the cable;

[0060] An electromagnetic excitation module is configured to generate a high-frequency alternating electromagnetic field inside the cable, wherein the frequency range of the high-frequency alternating electromagnetic field covers the resonant frequency interval of the conductor and the insulation layer of the cable;

[0061] a load module configured to be fixed to an end portion of the cable and to apply a multi-axial bending load to the cable;

[0062] an optical imaging module configured to surround the circumference of the cable at preset angular intervals and collect light reflection data from the cable surface by strobing, wherein the strobing frequency is configured to be an integer multiple of the electromagnetic excitation frequency of the electromagnetic excitation module;

[0063] The defect analysis module is configured to obtain light reflection data when receiving alternating excitation under different bending loads, and determine the defects of the cable based on the fluorescent deformation points and thermal deformation points of the fluorescent layer.

[0064] The central processing unit is configured to connect to each module to obtain signal responses and requests from each module.

[0065] As a preferred embodiment, a plurality of fluorescent deformation regions are arranged at intervals in the fluorescent layer, and one or more movable fluorescent particles are arranged in each of the fluorescent deformation regions.

[0066] As a preferred embodiment, the defect analysis module is configured to perform the following steps:

[0067] Step S1: Dynamic image sequence capture

[0068] During the electromagnetic excitation process, a deformation image sequence of the fluorescent deformation area is collected at a rate of more than 5000 frames per second;

[0069] Synchronously record the displacement and electromagnetic field intensity parameters of the bending loading module;

[0070] Step S2: Optical feature enhancement processing

[0071] Separate the polarization components of the original image and extract the polarization component image parallel to the normal direction of the cable surface;

[0072] Based on the thermochromic characteristics, the areas in the image that exceed the material yield strength are identified and marked as abnormal areas in the red channel;

[0073] Step S3: Deformation trajectory analysis

[0074] Three adjacent frames of images are selected to form an analysis unit, and the motion vector of the marker layer particles is calculated using a pixel displacement tracking algorithm;

[0075] Generate a displacement-time curve for each particle and screen out abnormal vibration points whose amplitude exceeds the safety threshold;

[0076] Step S4: Defect type identification

[0077] For surface crack defects: identify the discontinuous jump points appearing in the trajectory of the fluorescent particles and detect the extension length of the jump points along the cable axis;

[0078] For internal bubble defects: analyze the phase lag of particle vibration. When the phase difference of five consecutive analysis units in the same area exceeds 30%, it is determined to be an internal cavity.

[0079] For material fatigue defects: the geometric topology of the abnormal area in the red channel is extracted. If the area shows a dendritic fractal structure and the area accounts for more than 0.5%, it is determined to be material failure;

[0080] Step S5: 3D defect reconstruction

[0081] Fuse circumferential multi-camera view data to reconstruct a 3D point cloud model of the defect area;

[0082] Defect levels are divided according to the curvature distribution of the point cloud, and areas where the curvature mutation exceeds 2 times the baseline value are marked as high-risk defects.

[0083] As a preferred embodiment, a collaborative control module is further included, which is configured to:

[0084] Automatically adjust the illumination angle of the LED array and the rotation angle of the polarizer according to the cable diameter;

[0085] When a surface crack is detected, the bending loading module is triggered to increase the flexural load in the axial direction to verify the defect propagation;

[0086] Generate a digital inspection report including defect location, type and 3D model.

[0087] As a preferred embodiment, the pixel displacement tracking algorithm in step S3 further includes:

[0088] A 20 × 20 pixel tracking window was established for each particle;

[0089] A pyramid hierarchical search strategy is used between adjacent frames, first performing coarse matching with a step size of 5 pixels, and then performing fine matching with a step size of 1 pixel;

[0090] When the matching error exceeds the preset threshold, fluorescence spectrum feature-assisted tracking is enabled, and the tracking path is corrected by comparing the RGB histogram distribution of the particles.

[0091] As a preferred method, the phase difference calculation in step S4 adopts:

[0092] Perform Hilbert transform on the vibration signal to generate an analytical signal;

[0093] Calculate the cosine similarity of the analytical signals of adjacent analysis units, and trigger a phase difference alarm when the similarity is lower than 0.7;

[0094] Combined with the electromagnetic field intensity change curve, phase fluctuations caused by external interference are eliminated.

[0095] As a preferred embodiment, the marking medium module further comprises:

[0096] Before spraying, the cable surface is plasma cleaned to control the surface roughness Ra value within the range of 0.8-1.2μm;

[0097] An electrostatic adsorption process is used to distribute the nanoparticles in a stripe shape along the circumference of the cable, with the stripe spacing being equivalent to the minimum detectable defect size.

[0098] As a preferred embodiment, the optical imaging module dynamically adjusts the stroboscopic duty cycle according to the ambient light intensity during image acquisition, and activates the optical chopper in a strong light environment to eliminate ambient light interference;

[0099] The deconvolution restoration algorithm is used for motion blurred images, and a pre-calibrated point spread function is used to deblur the image.

[0100] The beneficial effects of the embodiments of the present disclosure are:

[0101] Multi-dimensional detection capabilities have been significantly improved: the system integrates multiple functional modules, including a light source, an optical medium marking module, an electromagnetic excitation module, and a load module. The light source, through multiple sets of sub-light sources, a rotatable polarizer, and a wavelength adjustment unit, can illuminate the outer surface of the cable at a preset incident angle range. The optical imaging module collects light reflection data at multiple angles and high frequencies. The electromagnetic excitation module generates a high-frequency alternating electromagnetic field within a specific frequency range inside the cable, and the load module applies multi-axial bending loads, enabling multi-dimensional detection of surface and internal defects in the cable. Compared to traditional single detection methods, this system can more comprehensively detect defects in the cable under different conditions, greatly improving the accuracy and comprehensiveness of detection.

[0102] Defect detection accuracy has been significantly improved: The reflective marking layer set up by the optical medium marking module includes a fluorescent layer and a thermochromic layer. The fluorescent layer is spaced apart and contains multiple fluorescent deformation zones and movable fluorescent particles. Combined with a series of precise processing steps in the defect analysis module, such as high-speed image acquisition at more than 5,000 frames per second, polarization component separation, and pixel displacement tracking algorithms, it can accurately capture the tiny deformation and movement trajectory of the fluorescent particles. Different types of defects such as surface cracks, internal bubble defects, and material fatigue defects can be accurately identified and located through specific discrimination algorithms. At the same time, through three-dimensional defect reconstruction technology, a three-dimensional point cloud model of the defect area is generated and the defect level is divided, which brings the defect detection accuracy to a new level and effectively avoids the problems of missed detection and misjudgment caused by the lack of accuracy of traditional detection methods.

[0103] Significantly improved inspection efficiency: The optical imaging module performs stroboscopic data acquisition around the cable circumference at preset angle intervals, with the stroboscopic frequency being an integer multiple of the electromagnetic excitation frequency. This allows for rapid acquisition of light reflection data from the cable surface under varying conditions. The defect analysis module utilizes efficient image processing algorithms and automated defect identification procedures to rapidly analyze and process the collected data. Compared to traditional manual inspections and complex data analysis processes, this significantly shortens inspection time and improves efficiency, meeting the factory inspection requirements for large-scale cable production.

[0104] Enhanced adaptive detection capabilities: The collaborative control module can automatically adjust the LED array's illumination angle and polarizer rotation angle based on the cable diameter, enabling the system to adapt to the detection needs of cables of different specifications. When a surface crack is detected, the bending loading module is automatically triggered to increase the flexural load in that axis to verify the defect's extensibility, enabling intelligent and adaptive adjustment of the detection process. At the same time, a digital inspection report containing the defect location, type, and 3D model is generated to facilitate subsequent quality traceability and production improvements. Compared with traditional fixed parameter inspection methods, the system's versatility and practicality have been significantly improved.

[0105] Optimized anti-interference capabilities and image quality: The optical imaging module dynamically adjusts the strobe duty cycle based on ambient light intensity during image acquisition. In bright light environments, an optical chopper is activated to eliminate ambient light interference. A deconvolution restoration algorithm is used to deblur motion-blurred images, ensuring the clarity and accuracy of captured images. The marking medium module uses plasma cleaning and electrostatic adsorption processes to treat the cable surface, ensuring a better bond between the marking layer and the cable surface and improving the stability of the detection signal. These measures effectively enhance the system's anti-interference capabilities, ensuring high-quality detection data even in complex environments and improving the reliability of detection results.

[0106] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

[0107] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these effects are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described effects, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0108] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions 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 in parallel, or 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 dedicated hardware-based devices that perform the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A cable factory inspection system based on image recognition technology, characterized in that: include: A light source is configured to be arranged along the axial direction of the cable, and includes multiple groups of sub-light sources, each of which is provided with a corresponding rotatable polarizer and a wavelength adjustment unit, the wavelength adjustment unit being used to adjust the wavelength band of the light of the sub-light source, and each of the sub-light sources irradiates the outer surface of the cable at a preset incident angle range; An optical medium marking module is configured to apply a reflective marking layer to the surface of the cable, wherein the reflective marking layer includes a fluorescent layer and a thermochromic layer, and the color threshold of the thermochromic layer is equal to the yield strength of the outer sheath material of the cable; An electromagnetic excitation module is configured to generate a high-frequency alternating electromagnetic field inside the cable, wherein the frequency range of the high-frequency alternating electromagnetic field covers the resonant frequency interval of the conductor and the insulation layer of the cable; a load module configured to be fixed to an end portion of the cable and to apply a multi-axial bending load to the cable; an optical imaging module configured to surround the circumference of the cable at preset angular intervals and collect light reflection data from the cable surface by strobing, wherein the strobing frequency is configured to be an integer multiple of the electromagnetic excitation frequency of the electromagnetic excitation module; The defect analysis module is configured to obtain light reflection data when receiving alternating excitation under different bending loads, and determine the defects of the cable based on the fluorescent deformation points and thermal deformation points of the fluorescent layer. The central processing unit is configured to connect to each module to obtain signal responses and requests from each module.

2. The cable factory inspection system based on image recognition technology according to claim 1 is characterized in that: A plurality of fluorescent deformation regions are arranged at intervals in the fluorescent layer, and one or more movable fluorescent particles are arranged in each of the fluorescent deformation regions.

3. The cable factory inspection system based on image recognition technology according to claim 1 is characterized in that: The defect analysis module is configured to perform the following steps: Step S1: Dynamic image sequence capture During the electromagnetic excitation process, a deformation image sequence of the fluorescent deformation area is collected at a rate of more than 5000 frames per second; Synchronously record the displacement and electromagnetic field intensity parameters of the bending loading module; Step S2: Optical feature enhancement processing Separate the polarization components of the original image and extract the polarization component image parallel to the normal direction of the cable surface; Based on the thermochromic characteristics, the areas in the image that exceed the material yield strength are identified and marked as abnormal areas in the red channel; Step S3: Deformation trajectory analysis Three adjacent frames of images are selected to form an analysis unit, and the motion vector of the marker layer particles is calculated using a pixel displacement tracking algorithm; Generate a displacement-time curve for each particle and screen out abnormal vibration points whose amplitude exceeds the safety threshold; Step S4: Defect type identification For surface crack defects: identify the discontinuous jump points appearing in the trajectory of the fluorescent particles and detect the extension length of the jump points along the cable axis; For internal bubble defects: analyze the phase lag of particle vibration. When the phase difference of five consecutive analysis units in the same area exceeds 30%, it is determined to be an internal cavity. For material fatigue defects: the geometric topology of the abnormal area in the red channel is extracted. If the area shows a dendritic fractal structure and the area accounts for more than 0.5%, it is determined to be material failure; Step S5: 3D defect reconstruction Fuse circumferential multi-camera view data to reconstruct a 3D point cloud model of the defect area; Defect levels are divided according to the curvature distribution of the point cloud, and areas where the curvature mutation exceeds 2 times the baseline value are marked as high-risk defects.

4. The cable factory inspection system based on image recognition technology according to claim 3 is characterized in that: It also includes a collaborative control module configured to: Automatically adjust the illumination angle of the LED array and the rotation angle of the polarizer according to the cable diameter; When a surface crack is detected, the bending loading module is triggered to increase the flexural load in the axial direction to verify the defect propagation; Generate a digital inspection report including defect location, type and 3D model.

5. The cable factory inspection system based on image recognition technology according to claim 3 is characterized in that: The pixel displacement tracking algorithm in step S3 further includes: A 20 × 20 pixel tracking window was established for each particle; A pyramid hierarchical search strategy is used between adjacent frames, first performing coarse matching with a step size of 5 pixels, and then performing fine matching with a step size of 1 pixel; When the matching error exceeds the preset threshold, fluorescence spectrum feature-assisted tracking is enabled, and the tracking path is corrected by comparing the RGB histogram distribution of the particles.

6. The cable factory inspection system based on image recognition technology according to claim 3 is characterized in that: The phase difference calculation in step S4 is: Perform Hilbert transform on the vibration signal to generate an analytical signal; Calculate the cosine similarity of the analytical signals of adjacent analysis units, and trigger a phase difference alarm when the similarity is lower than 0.7; Combined with the electromagnetic field intensity change curve, phase fluctuations caused by external interference are eliminated.

7. The cable factory inspection system based on image recognition technology according to claim 1 is characterized in that: The marking medium module further comprises: Before spraying, the cable surface is plasma cleaned to control the surface roughness Ra value within the range of 0.8-1.2μm; An electrostatic adsorption process is used to distribute the nanoparticles in a stripe shape along the circumference of the cable, with the stripe spacing being equivalent to the minimum detectable defect size.

8. The cable factory inspection system based on image recognition technology according to claim 1 is characterized in that: The optical imaging module dynamically adjusts the stroboscopic duty cycle according to the ambient light intensity during image acquisition, and activates the optical chopper in a strong light environment to eliminate ambient light interference; The deconvolution restoration algorithm is used for motion blurred images, and a pre-calibrated point spread function is used to deblur the image.

Images