Submarine cable wrapping tape defect detection system and detection method

Abstract

The invention discloses a submarine cable wrapping tape defect detection system and detection method, and belongs to the technical field of submarine cable detection. The submarine cable wrapping tape defect detection system comprises an image acquisition unit, a wrapping tape positioning unit, an image processing unit, a wrapping tape defect identification unit, a control unit, a data storage unit and an alarm unit, and the image acquisition unit, the wrapping tape positioning unit and the alarm unit are arranged on a wrapping machine; the image acquisition unit and the wrapping tape positioning unit are in telecommunication connection with the image processing unit, the image processing unit is in telecommunication connection with the wrapping tape defect identification unit, the defect identification unit is in telecommunication connection with the control unit, and the control unit is in telecommunication connection with the data storage unit and the alarm unit.

Description

Technical Field

[0001] The present invention relates to the technical field of submarine cable detection, and in particular to a submarine cable wrapping tape defect detection system and a detection method. Background Art

[0002] In the traditional submarine cable manufacturing process, the wrapping tape is an important component of the submarine cable, and its quality is directly related to the overall performance of the submarine cable and the service life of the final product. However, during the wrapping production process, the wrapping tape may have various defects such as breakage, dislocation, uneven thickness, etc. If these problems are not discovered and handled in time, they will seriously affect the quality of the submarine cable and the economic benefits of the enterprise.

[0003] At present, most submarine cable manufacturers still use traditional manual visual inspection to check the quality of the cable wrapping tape after wrapping. Although this method is intuitive and easy to implement, it has many shortcomings in practical applications. On the one hand, the speed of manual inspection is limited by the operator's work efficiency and cannot meet the needs of high-speed continuous operation on modern production lines; on the other hand, due to differences in people's subjective judgment and the influence of the working environment (such as fatigue, light changes, etc.), the accuracy of the inspection results is difficult to guarantee, which is prone to false detection or missed detection.

[0004] With the continuous improvement of industrial automation and intelligence, traditional manual inspection methods have become increasingly difficult to meet the requirements of modern manufacturing for product quality control. Therefore, it is urgent to develop a submarine cable wrapping tape defect detection system and detection method to achieve automatic identification, positioning, recording and early warning of various defects in the production process, thereby improving production quality and efficiency. Summary of the invention

[0005] The object of the present invention is to provide a submarine cable wrapping tape defect detection system and detection method to solve the technical problems mentioned in the above background technology.

[0006] The technical solution for achieving the purpose of the present invention is:

[0007] A submarine cable wrapping tape defect detection system comprises an image acquisition unit, a wrapping tape positioning unit, an image processing unit, a wrapping tape defect recognition unit, a control unit, a data storage unit, and an alarm unit, wherein the image acquisition unit, the wrapping tape positioning unit, and the alarm unit are arranged on a submarine cable wrapping production line; the image acquisition unit, the wrapping tape positioning unit and the image processing unit are connected by telecommunication, the image processing unit is connected by telecommunication to the wrapping tape defect recognition unit, the defect recognition unit is connected by telecommunication to the control unit, and the control unit is connected by telecommunication to the data storage unit and the alarm unit.

[0008] The submarine cable wrapping tape defect detection system of the present invention is provided by setting up an image acquisition unit, a wrapping tape positioning unit, an image processing unit, a wrapping tape defect recognition unit, a control unit, a data storage unit, and an alarm unit, wherein the image acquisition unit, the wrapping tape positioning unit and the image processing unit are connected by telecommunication, the image processing unit is connected by telecommunication to the wrapping tape defect recognition unit, the defect recognition unit is connected by telecommunication to the control unit, and the control unit is connected by telecommunication to the data storage unit and the alarm unit. When the system is running, the image acquisition unit and the wrapping tape positioning unit firstly collect wrapping images and position information in real time respectively; then, the image processing unit pre-processes the collected wrapping images and integrates them with the position information obtained by the wrapping tape positioning unit to form wrapping image information; next, The image processing unit transmits this information to the wrapping tape defect recognition unit, which is responsible for detecting whether there are wrapping defects on the wrapping layer of the submarine cable after wrapping, and classifies the defective wrapping image information according to the specific manifestation of the defect when the wrapping defect is detected. The classified defect information will be transmitted to the control unit, and the control unit will then send an alarm instruction to the alarm unit; at the same time, the control unit will also store the classified defect image information including time, batch information and alarm records in the data storage unit; this system integrates image acquisition, processing, analysis and alarm functions, and realizes automatic, efficient and accurate detection of wrapping tape defects on the submarine cable wrapping layer, significantly improving the detection efficiency and accuracy, and reducing product quality problems caused by wrapping tape defects. The application of automated detection methods not only reduces labor costs, but also reduces the possible impact of human factors on the detection results.

[0009] Furthermore, the submarine cable wrapping tape defect detection system also includes a host computer, which is electrically connected to the control unit; when a wrapping defect is detected, the control unit will transmit specific information of the defect to the host computer, and this information will be displayed on the operation interface of the host computer; the host computer can directly retrieve the defect image information, position information, alarm information and related production information of the wrapping tape from the data storage unit through the electrical signal connection between the control unit and the data storage unit, which enables technicians to evaluate and analyze the detection effect of the system based on these detailed feedback information, and then optimize and adjust the submarine cable wrapping tape defect detection system and method thereof. In this way, it not only helps to improve the production process of the submarine cable wrapping layer, but also further improves the overall quality of the product.

[0010] Furthermore, the submarine cable wrapping tape defect detection system also includes a wrapping machine, and the switch of the wrapping machine is electrically connected to the control unit; when a wrapping defect is detected, the control unit will immediately issue an instruction to stop the wrapping machine by controlling the switch of the wrapping machine so that technicians can repair the defective wrapping tape in time, ensuring that the production line can respond quickly when a problem is found, avoiding the continued production of defective products, thereby effectively improving product quality and production efficiency.

[0011] Furthermore, the image acquisition unit and the wrapping tape positioning unit are arranged at the same position of the submarine cable wrapping production line to ensure that the pictures acquired by the image acquisition unit correspond one to one with the position information acquired by the wrapping tape positioning unit, thereby ensuring a high degree of synchronization between the image data and the position data, thereby making subsequent image processing and defect identification more accurate and reliable.

[0012] The present invention also provides a method for detecting defects in a submarine cable wrapping tape, comprising the following steps:

[0013] (1) Constructing a database of wrapping tape defects;

[0014] (2) A convolutional neural network method was used to construct a deep learning architecture based on wrapping tape defect image processing, recognition, and defect classification, and a wrapping tape defect recognition unit was trained using a wrapping tape defect image database;

[0015] (3) After completing the learning and training, the wrapping tape that has been wrapped around the submarine cable is imaged, positioned, preprocessed, and the wrapping tape defects are detected and identified. At the same time, an alarm is issued and the wrapping machine is shut down. The monitored wrapping tape defect image information, location information, alarm information, and production information are stored in the wrapping tape defect image database, and the wrapping tape defect image database is updated.

[0016] The method for detecting defects in the submarine cable wrapping tape of the present invention constructs a wrapping tape defect image database, and adopts a convolutional neural network (CNN) method to construct a deep learning architecture specifically for wrapping tape defect image processing, recognition and classification. By using the wrapping tape defect image database for training, the architecture can utilize advanced image processing technology and machine learning algorithms to significantly improve the accuracy and speed of defect detection. This method can adapt to submarine cables of different types and specifications, has excellent versatility and scalability, and provides strong technical support for quality control of submarine cable production.

[0017] Furthermore, the steps for constructing the wrapping tape defect image database in step (1) are as follows: first, using an image acquisition unit to acquire images of the wrapping tape that has been wrapped around the submarine cable; having technical workers of the submarine cable wrapping production line classify the acquired images into images with wrapping defects and images without wrapping defects, and further subdividing the images with wrapping defects according to the manifestation of the wrapping defects; on this basis, establishing a wrapping tape defect image database; after using the image acquisition unit to acquire the wrapping tape images in real time, technical workers of the submarine cable wrapping production line will manually classify these acquired images, and based on these classification results, establish a wrapping tape defect image database, which provides accurate learning and training data support for the wrapping tape defect recognition unit, ensuring that the deep learning model can learn the characteristics of the wrapping tape defects from a large amount of labeled data, thereby improving the accuracy and reliability of defect recognition. This method not only enhances the intelligent recognition capability of the system, but also lays a solid foundation for subsequent automatic detection.

[0018] Furthermore, the wrapping defects may manifest themselves in the form of loose wrapping, warping, wrinkling, fuzzing, breakage, and scratches.

[0019] Furthermore, the preprocessing includes using an image processing unit to process the collected wrapping tape images, and integrating the processed images with the positioning information in a one-to-one correspondence.

[0020] Furthermore, the image processing includes denoising, contrast enhancement, and grayscale conversion.

[0021] The present invention adopts an image processing unit to perform image processing operations such as denoising, contrast enhancement, and grayscale on the collected wrapping tape images. Through these preprocessing steps, the image quality can be effectively optimized to ensure that the collected images are clear and distortion-free, providing a more reliable data basis for subsequent defect identification and improving the efficiency and accuracy of subsequent processing.

[0022] Furthermore, the submarine cable wrapping tape defect detection method also includes using a convolutional neural network method to continue to use the updated wrapping tape defect image database to further learn and train the wrapping tape defect recognition unit; according to actual production conditions, image acquisition parameters, processing algorithm parameters, alarm thresholds and other settings can be adjusted to enhance the flexibility and adaptability of the system. With the continuous updating of the wrapping tape defect image database, the defect recognition unit can continuously learn and train, and optimize the recognition algorithm according to the new detection results, thereby further improving the efficiency and accuracy of detection. This method not only enhances the self-improvement capability of the system, but also ensures that the system can adapt to different types of submarine cables and changing production environments.

[0023] By adopting the above technical solution, the present invention has the following beneficial effects:

[0024] (1) The submarine cable wrapping tape defect detection system of the present invention is provided with an image acquisition unit, a wrapping tape positioning unit, an image processing unit, a wrapping tape defect recognition unit, a control unit, a data storage unit, and an alarm unit, wherein the image acquisition unit, the wrapping tape positioning unit and the image processing unit are connected by telecommunication, the image processing unit is connected by telecommunication to the wrapping tape defect recognition unit, the defect recognition unit is connected by telecommunication to the control unit, and the control unit is connected by telecommunication to the data storage unit and the alarm unit. When the system is running, the image acquisition unit and the wrapping tape positioning unit firstly respectively acquire the wrapping image and position information of the wrapped submarine cable in real time; then, the image processing unit pre-processes the acquired wrapping image and integrates it with the position information acquired by the wrapping tape positioning unit. , forming wrapping image information; next, the image processing unit transmits this information to the wrapping tape defect recognition unit, which is responsible for detecting whether there are wrapping defects, and when wrapping defects are detected, the defective wrapping image information is classified according to the specific manifestation of the defect, and the classified defect information is transmitted to the control unit, and the control unit immediately sends an alarm instruction to the alarm unit; at the same time, the control unit will also store the classified defect image information including time, batch information and alarm records in the data storage unit; this system integrates image acquisition, processing, analysis and alarm functions, and realizes automatic, efficient and accurate detection of submarine cable wrapping tape defects, significantly improving detection efficiency and accuracy, and reducing product quality problems caused by wrapping tape defects. Through the application of automated detection methods, not only the labor cost is reduced, but also the possible impact of human factors on the detection results is reduced.

[0025] (2) The submarine cable wrapping tape defect detection system of the present invention also includes a host computer, which is connected to the control unit by telecommunication. When a wrapping defect is detected, the control unit will transmit specific information of the defect to the host computer, and the information will be displayed on the operation interface of the host computer. The host computer can directly retrieve the defect image information, position information, alarm information and related production information of the wrapping tape from the data storage unit through the electrical signal connection between the control unit and the data storage unit, so that the technicians can evaluate and analyze the detection effect of the system based on these detailed feedback information, and then optimize and adjust the submarine cable wrapping tape defect detection system and method thereof. In this way, it not only helps to improve the production process, but also further improve the overall quality of the product.

[0026] (3) The submarine cable wrapping tape defect detection system of the present invention also includes a wrapping machine, and the switch of the wrapping machine is electrically connected to the control unit; when a wrapping defect is detected, the control unit will immediately issue an instruction to stop the wrapping machine by controlling the switch of the wrapping machine so that the technicians can repair the defective wrapping tape in time, ensuring that the production line can respond quickly when a problem is found, avoiding the continued production of defective products, thereby effectively improving product quality and production efficiency.

[0027] (4) The image acquisition unit and the wrapping tape positioning unit of the present invention are arranged at the same position of the submarine cable wrapping production line to ensure that the image acquired by the image acquisition unit corresponds to the position information acquired by the wrapping tape positioning unit, thereby ensuring a high degree of synchronization between the image data and the position data, thereby making subsequent image processing and defect identification more accurate and reliable.

[0028] (5) The method for detecting defects in the submarine cable wrapping tape of the present invention constructs a wrapping tape defect image database and adopts a convolutional neural network (CNN) method to construct a deep learning architecture specifically for wrapping tape defect image processing, recognition and classification. By training the wrapping tape defect image database, the architecture can utilize advanced image processing technology and machine learning algorithms to significantly improve the accuracy and speed of defect detection. This method can adapt to submarine cables of different types and specifications, has excellent versatility and scalability, and provides strong technical support for quality control of submarine cable production.

[0029] (6) After the present invention uses the image acquisition unit to collect the wrapping tape images in real time, the technical workers of the submarine cable wrapping production line will manually classify the collected images. Based on these classification results, a wrapping tape defect image database is established. The database provides accurate learning and training data support for the wrapping tape defect recognition unit, ensuring that the deep learning model can learn the characteristics of wrapping tape defects from a large amount of labeled data, thereby improving the accuracy and reliability of defect recognition. This method not only enhances the intelligent recognition capability of the system, but also lays a solid foundation for subsequent automatic detection.

[0030] (7) The present invention uses an image processing unit to perform image processing operations such as denoising, contrast enhancement, and grayscale on the collected wrapping tape images. Through these preprocessing steps, the image quality can be effectively optimized to ensure that the collected images are clear and distortion-free, providing a more reliable data basis for subsequent defect identification and improving the efficiency and accuracy of subsequent processing.

[0031] (8) The method for detecting defects in the wrapping tape of the present invention further includes using a convolutional neural network method to continue to use the updated wrapping tape defect image database for further learning and training; according to actual production conditions, image acquisition parameters, processing algorithm parameters, alarm thresholds and other settings can be adjusted to enhance the flexibility and adaptability of the system. With the continuous updating of the wrapping tape defect image database, the defect recognition unit can continuously learn and train, and optimize the recognition algorithm according to the new detection results, thereby further improving the efficiency and accuracy of the detection. This method not only enhances the self-improvement capability of the system, but also ensures that the system can adapt to different types of submarine cables and changing production environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments and in conjunction with the accompanying drawings, wherein

[0033] Figure 1 It is a schematic diagram of the process of the submarine cable wrapping tape defect detection system of the present invention. DETAILED DESCRIPTION

[0034] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0037] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0038] In the description of the embodiments of the present invention, it should be understood that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, or are the orientations or positional relationships in which the inventive product is conventionally placed when used, or are the orientations or positional relationships conventionally understood by those skilled in the art. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0039] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention is further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0040] (Example)

[0041] See Figure 1A submarine cable wrapping tape defect detection system comprises an image processing unit, a wrapping tape defect recognition unit, a control unit, a data storage unit, a wrapping machine, a host computer, an alarm unit arranged on the wrapping machine, an image acquisition unit, and a wrapping tape positioning unit; the image acquisition unit and the wrapping tape positioning unit are arranged at the same production position of the wrapping machine, and the wrapping image and position are collected in real time; the image acquisition unit and the wrapping tape positioning unit are connected to the image processing unit by telecommunication, and the image processing unit pre-processes the collected wrapping image of the wrapped submarine cable, and integrates it with the position information obtained by the wrapping tape positioning unit to form wrapping image information; the image processing unit is connected to the wrapping tape defect recognition unit by telecommunication, and the wrapping image is collected by the wrapping tape positioning unit. The information is transmitted to the wrapping tape defect recognition unit, which is responsible for detecting whether there is a wrapping defect, and classifying the defective wrapping image information according to the specific manifestation of the defect when a wrapping defect is detected; the defect recognition unit is telecommunication-connected to the control unit, and the control unit is telecommunication-connected to the data storage unit, the wrapping machine, the alarm unit, and the host computer. The defect recognition unit transmits the classified wrapping image information with wrapping defects to the control unit, and the control unit issues a stop command and an alarm command to the wrapping machine and the alarm unit respectively, and sends an alarm signal and specific defect information to the host computer, and at the same time stores the classified wrapping image information with wrapping defects, as well as the corresponding time, batch, and alarm records in the data storage unit.

[0042] A method for detecting defects in a submarine cable wrapping tape based on a submarine cable wrapping tape defect detection system comprises the following steps:

[0043] (1) Constructing a database of wrapping tape defects;

[0044] Specifically, firstly, an image acquisition unit is used in combination with appropriate lighting to acquire the image of the wrapping tape on the wrapped submarine cable; then, a wrapping tape positioning unit arranged on the production line is used to accurately record the position information of the wrapping tape; a skilled worker of the submarine cable wrapping production line classifies the acquired images into images with wrapping defects and images without wrapping defects, and further subdivides the images with wrapping defects according to the manifestation of the wrapping defects; the manifestation of wrapping defects includes loose package, warping, wrinkles, fuzzing, fracture, and scratches; on this basis, a wrapping tape defect image database is established, and databases are established for wrapping tape defect images of different defect types respectively, and the image, classification result, corresponding position information, time and other data of each detection are stored in the database of wrapping tape defect images of each defect type; the image acquisition unit of this embodiment is preferably a high-resolution high-speed camera; the wrapping tape positioning unit is preferably a combination of a meter counter and a motor encoder, and the meter counter is combined with the motor encoder data of the wrapping machine to accurately record the position information of the wrapping tape;

[0045] (2) A convolutional neural network method was used to construct a deep learning architecture based on wrapping tape defect image processing, recognition, and defect classification, and a wrapping tape defect recognition unit was trained using a wrapping tape defect image database;

[0046] Specifically, in the process of running the training model, the wrapping tape defect recognition unit amplifies a large amount of image information including wrapping tape without defects and various wrapping tape defects processed by the wrapping tape image processing unit, and encodes the amplified image information to construct a wrapping tape defect classification reference standard of the running working model of the wrapping tape defect recognition unit;

[0047] (3) After completing the learning and training, the cable wrapping tape is inspected, identified and located, and an alarm and wrapping machine shutdown operation are performed. The monitored wrapping tape defect image information, location information, alarm information, and production information are stored in the wrapping tape defect image database, and the wrapping tape defect image database is updated. The convolutional neural network method continues to use the updated wrapping tape defect image database to train the wrapping tape defect recognition unit;

[0048] Specifically, in the process of running the working model, the wrapping tape defect recognition unit amplifies the actual image collected after being processed by the image processing unit, and encodes the image information and the positioning information corresponding to the image collected by the wrapping tape positioning unit, compares the coded information with the constructed wrapping tape defect classification reference standard, and determines whether there is a winding defect and the specific type of the winding defect. If there is a defect, the wrapping tape defect information such as image, position, type, time and other data are stored in the wrapping tape defect image database, and the alarm unit is triggered to alarm, and an instruction is issued to the wrapping machine to stop, and an alarm signal is sent to the upper computer through the network to prompt the operator to handle it. At the same time, the corresponding alarm information is stored in the wrapping tape defect image database, and the wrapping tape defect image database is updated;

[0049] (4) The convolutional neural network method is used to continue to use the updated wrapping tape defect image database for further learning and training; according to the actual production situation, the image acquisition parameters, processing algorithm parameters and alarm threshold settings can be adjusted to improve the flexibility and adaptability of the system. With the continuous update of the wrapping tape defect image database, the defect recognition unit can continuously learn and train, optimize the recognition algorithm according to the new detection results, thereby further improving the efficiency and accuracy of detection. This method not only enhances the self-improvement ability of the system, but also ensures that the system can adapt to different types of submarine cables and changing production environments.

[0050] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A submarine cable wrapping tape defect detection system, characterized in that: It includes an image acquisition unit, a wrapping tape positioning unit, an image processing unit, a wrapping tape defect recognition unit, a control unit, a data storage unit, and an alarm unit, wherein the image acquisition unit, the wrapping tape positioning unit, and the alarm unit are arranged on a submarine cable wrapping production line; the image acquisition unit, the wrapping tape positioning unit, and the image processing unit are connected by telecommunication to the wrapping tape defect recognition unit, the image processing unit is connected by telecommunication to the wrapping tape defect recognition unit, the defect recognition unit is connected by telecommunication to the control unit, and the control unit is connected by telecommunication to the data storage unit and the alarm unit.

2. The submarine cable wrapping tape defect detection system according to claim 1 is characterized in that: The submarine cable wrapping tape defect detection system also includes a host computer, which is connected to the control unit by telecommunication.

3. The submarine cable wrapping tape defect detection system according to claim 1 is characterized in that: The submarine cable wrapping tape defect detection system also includes a wrapping machine, and a switch of the wrapping machine is connected to the control unit by telecommunication.

4. The submarine cable wrapping tape defect detection system according to claim 3 is characterized in that: The image acquisition unit and the wrapping tape positioning unit are arranged at the same position of the submarine cable wrapping production line.

5. A method for detecting defects in a submarine cable wrapping tape based on the submarine cable wrapping tape defect detection system according to any one of claims 1 to 4, characterized in that: The following steps are involved: (1) Constructing a database of wrapping tape defects; (2) A convolutional neural network method was used to construct a deep learning architecture based on wrapping tape defect image processing, recognition, and defect classification, and a wrapping tape defect recognition unit was trained using a wrapping tape defect image database; (3) After completing the learning and training, the wrapping tape that has been wrapped around the submarine cable is imaged, positioned, preprocessed, and the wrapping tape defects are detected and identified. At the same time, an alarm is issued and the wrapping machine is shut down. The monitored wrapping tape defect image information, location information, alarm information, and production information are stored in the wrapping tape defect image database, and the wrapping tape defect image database is updated.

6. The method for detecting defects in submarine cable wrapping tape according to claim 5, characterized in that: The steps of constructing the wrapping tape defect image database in step (1) are as follows: first, using an image acquisition unit, the wrapping tape image of the submarine cable wrapped with the wrapping tape is collected; the technical workers of the submarine cable wrapping production line classify the collected images into images with wrapping defects and images without wrapping defects, and further subdivide the images with wrapping defects according to the manifestation of the wrapping defects; on this basis, a wrapping tape defect image database is established.

7. The method for detecting defects in submarine cable wrapping tape according to claim 6, characterized in that: The wrapping defects include loose wrapping, warping, wrinkles, fuzzing, breakage and scratches.

8. The method for detecting defects in submarine cable wrapping tape according to claim 5, characterized in that: The preprocessing includes using an image processing unit to process the collected wrapping tape images, and integrating the processed images with the positioning information in a one-to-one correspondence.

9. The method for detecting defects in submarine cable wrapping tape according to claim 5, characterized in that: The image processing includes denoising, contrast enhancement, and grayscale conversion.

10. The method for detecting defects in submarine cable wrapping tape according to claim 5, characterized in that: The submarine cable wrapping tape defect detection method also includes using a convolutional neural network method to continue to use the updated wrapping tape defect image database to further learn and train the wrapping tape defect recognition unit.

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