A method, device, medium and product for detecting needle mark edge in chip Pad

Abstract

The embodiments of the present application relate to the field of chip detection technology, and disclose a method, device, medium and product for detecting the edge of a needle mark in a chip Pad. The method includes collecting and making a template image; obtaining a training image, processing the training image by a LINE-MOD algorithm, and obtaining a feature vector and feature point coordinates of the training image; obtaining a picture to be detected, matching the picture to be detected with the template image by a LINE-MOD algorithm, and outputting a detection image; processing the detection image by an adaptive threshold segmentation algorithm; segmenting the needle mark area of ​​the detection image, and calculating the size spacing between the needle mark area and the Pad frame, and outputting the detection result. It can at least be used to solve the technical problem of insufficient accuracy in the Pad detection process.

Description

Technical Field

[0001] The present application relates to the field of chip detection technology, and in particular to a method, device, medium and product for detecting a needle mark edge in a chip Pad. Background Art

[0002] In the chip manufacturing process, packaging test is one of the last few key steps, its purpose is to ensure that the chip can work properly and protect it from physical and environmental damage. The packaging test process includes multiple steps, such as wafer testing, cutting, mounting, bonding and packaging. Among these steps, wafer probe test (CP site) is an important link, which is used to test the electrical performance of each chip on the wafer before the wafer is cut into individual chips.

[0003] At the CP site, defects such as pressure damage and abnormal needle marks in the pad may occur during the probe contact process. At present, these defects are mainly identified by manual inspection. This method has some significant disadvantages, such as low man-machine ratio, low detection efficiency, and easy to have false detection and missed detection. Therefore, a more efficient and accurate detection method is urgently needed to improve the accuracy and efficiency of detection and reduce manual intervention.

[0004] Existing Pad detection methods mainly rely on traditional computer vision techniques, which are usually based on predefined rules and thresholds for edge detection and image segmentation. These methods work well when dealing with simple and regular patterns, but are easily disturbed when faced with complex backgrounds and lighting changes, resulting in reduced detection accuracy. In addition, the detection accuracy of traditional algorithms is limited, and it is easy to miss or misdetect. Summary of the invention

[0005] One purpose of the present application is to provide a method, device, medium and product for detecting needle marks on a chip Pad, at least to solve the problem of insufficient accuracy in the Pad detection process.

[0006] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0007] In the first aspect, some embodiments of the present application also provide a method for detecting the edge of a needle mark in a chip Pad, including collecting and making a template image; obtaining a training image, processing the training image through a LINE-MOD algorithm, and obtaining a feature vector and feature point coordinates of the training image; saving the feature vector and feature point coordinates to the corresponding template image; obtaining a picture to be detected, matching the picture to be detected with the template image through a LINE-MOD algorithm, and outputting a detection image; the detection image includes the matched Pad information, position and confidence; processing the detection image through an adaptive threshold segmentation algorithm; segmenting the needle mark area of ​​the detection image, and calculating the size spacing between the needle mark area and the Pad border, and outputting the detection result.

[0008] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0009] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0010] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0011] Compared with the related art, the solution provided in the embodiment of the present application is based on the LINE-MOD image template matching method for detecting the edge of the needle mark in the pad. By utilizing advanced image processing and template matching algorithms, it can effectively improve the detection accuracy and efficiency, reduce the burden of manual detection, and improve the production capacity of the entire chip manufacturing process. This method is not only applicable to the existing production environment, but also can achieve high-precision detection of pad needle marks without relying on large-scale data annotation, which is very suitable for data collection conditions in industrial scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0013] Figure 1 A schematic diagram of a flow chart of a method for detecting a needle mark edge in a chip Pad provided according to an embodiment of the present application;

[0014] Figure 2 A chip template picture provided according to an embodiment of the present application;

[0015] Figure 3 A chip template image with feature information provided according to an embodiment of the present application;

[0016] Figure 4 A chip detection picture provided according to an embodiment of the present application;

[0017] Figure 5 The present invention is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] First embodiment

[0020] The first embodiment of the present application relates to a method for detecting a needle mark edge in a chip Pad. Figure 1 As shown, the method may include the following steps:

[0021] S101, collecting and making template images;

[0022] S102, obtaining a training picture, processing the training picture by using the LINE-MOD algorithm to obtain a feature vector and feature point coordinates of the training picture; and saving the feature vector and feature point coordinates to the corresponding template picture;

[0023] S103, obtaining a picture to be detected, matching the picture to be detected with the template picture by using the LINE-MOD algorithm, and outputting a detection picture; the detection picture includes the matched Pad information, position and confidence;

[0024] S104, processing the detection image by using an adaptive threshold segmentation algorithm; segmenting the needle mark area of ​​the detection image, calculating the size spacing between the needle mark area and the pad frame, and outputting the detection result.

[0025] The following is a detailed description of each of the above steps.

[0026] Step S101, collect and make a template image; you can select a stable sample chip according to the type of chip and specific needs. The sample chip should be of good quality and have no obvious defects on the surface to ensure the high accuracy and reliability of the template image. Use a high-resolution industrial camera to capture images of the sample chip. During the acquisition process, ensure that the lighting is uniform to avoid interference from reflections and shadows. The captured image should cover the entire area of ​​the chip, especially the Pad area. Fix the sample chip on the image acquisition platform, and adjust the position and angle of the camera and light source to obtain the best acquisition effect. Perform multiple image acquisitions, and select a group of images with the best quality as the template image. Alternatively, depending on the type of chip, use industrial mapping software to draw a template image of the chip to obtain a noise-free or low-noise template image, such as Figure 2 shown.

[0027] Step S102, extract features from the training image using the LINE-MOD algorithm, including feature vectors and feature point coordinates. The LINE-MOD algorithm can efficiently extract representative feature points from the image. Feature points refer to the pads on the chip. Pads are small metal areas on the surface of the chip that are used to connect to external circuits. Generate corresponding feature vectors to ensure the accuracy and completeness of feature information. Save the extracted feature vectors and feature point coordinates to the corresponding template image. Figure 3 As shown in the figure, the association between the training image and the template image is established. This data storage method facilitates rapid retrieval and call in the subsequent matching process, improving the matching efficiency.

[0028] The storage and management of feature information can also provide data support for subsequent detection and analysis, facilitating the verification and improvement of detection results. The LINE-MOD algorithm comprehensively extracts and processes the feature vectors and feature points of the image during the training phase, allowing the algorithm to adapt to training images of different types and qualities. In practical applications, regardless of changes in the type of sample chips or different image acquisition conditions, the algorithm can provide stable feature extraction effects. This highly adaptable feature extraction enables the algorithm to maintain high detection accuracy and reliability in the face of complex industrial scenarios.

[0029] The feature vectors and feature point coordinates processed by the LINE-MOD algorithm provide high-quality feature information for subsequent template matching. During the template matching process, based on the feature information stored in the template image, high-precision matching can be achieved to accurately identify the pad area and its edge needle marks.

[0030] Step S103, obtain the picture to be detected and perform LINE-MOD algorithm matching, obtain the picture to be detected, and collect the image of the chip to be detected on the production line. Use a high-resolution industrial camera to shoot the chip to be detected to ensure that the image is clear, blur-free and noise-free. The acquisition process is similar to the acquisition of the template image, and the lighting conditions should be consistent to avoid reflections and shadows. The collected pictures to be detected are preprocessed to improve the accuracy and efficiency of subsequent matching. The preprocessing steps may include Gaussian blur, contrast adjustment, and image enhancement. Gaussian blur is performed on the picture to be detected to smooth the image and remove small noise. Contrast adjustment is performed to enhance the details in the image to make the Pad area clearer. Image enhancement algorithms, such as Laplace transform, are applied to further highlight image edges and details.

[0031] Use the LINE-MOD algorithm to match the preprocessed image to be tested with the template image created previously. Perform Gaussian blur and edge detection on the image to be tested, and extract feature points and feature vectors. Figure 4 As shown, it should include the matched Pad information (such as position and boundary), confidence, and possible needle mark area.

[0032] Through the LINE-MOD algorithm, the image to be detected can be matched with the template image quickly and effectively to determine the Pad area and its position; by matching feature points and feature vectors, the confidence of the matching result is output. This high-confidence matching result helps to reduce false detection and missed detection and improve the reliability of detection; during the matching process, the precise positioning of the Pad area is achieved through the calculation of the gradient response map and the similarity of the feature points. The output detection image can clearly mark the specific position and boundary of the Pad, providing an accurate reference for subsequent detection and analysis; the LINE-MOD algorithm shows good adaptability when processing images to be detected of different types and conditions. No matter how the shooting angle and lighting conditions of the image to be detected change, the algorithm can maintain a high matching accuracy; the automated matching process reduces manual intervention and improves detection efficiency. Through efficient algorithm matching, a large number of images to be detected can be quickly processed, improving the overall detection speed of the production line.

[0033] Step S104, processing the detection image through an adaptive threshold segmentation algorithm to segment the needle mark area, and calculating the size distance between the needle mark area and the pad frame, and finally outputting the detection result. An adaptive threshold segmentation algorithm is applied to the detection image to extract the needle mark area. The adaptive threshold segmentation algorithm dynamically adjusts the segmentation threshold according to the local features of the image to ensure the accuracy of the segmentation result; the adaptive threshold is calculated according to the local features of the detection image. The local mean, local variance or other local statistical features can be used as the basis for threshold calculation. The local window is used to traverse the entire image, and the local threshold is calculated in each window and applied to the corresponding image area; based on the calculated adaptive threshold, the image is binarized and the image is instance-segmented; after the segmentation is completed, the needle mark area is extracted, and the size spacing between the needle mark area and the pad border is calculated; the boundary information of the needle mark area is extracted, and the closest distance to the pad border is calculated, and the geometric features of the needle mark area (such as area, perimeter, etc.) and the minimum spacing from the needle mark area to the pad border are recorded; it is determined whether the geometric features of the needle mark area meet the preset detection standards (such as area, shape, etc.), and whether the minimum spacing between the needle mark area and the pad border meets the specified size requirements.

[0034] It is not difficult to find that compared with the related art, the solution provided in the embodiment of the present application adopts a LINE-MOD template matching algorithm for pads, and obtains pad information, position and confidence by sliding a window and training feature point information obtained through pre-collected noise-free or low-noise pad templates. In the pad area, the needle mark area is segmented by an adaptive threshold segmentation algorithm. The distance between the needle mark area and the pad border size is calculated, and the detection result is output according to the rule size requirements to improve the detection accuracy.

[0035] Second embodiment

[0036] The second embodiment of the present application relates to a method for detecting a needle mark edge in a chip Pad. The second embodiment is an improvement on the first embodiment, and the specific improvements are:

[0037] The LINE-Mod algorithm uses a template matching method. It collects template images of objects at various angles and distances, extracts, saves and encodes the gradient and normal vector direction features of each viewpoint on the template image. The LineMod algorithm includes feature point feature vector calculation, coordinate calculation and information storage in the training phase.

[0038] A template image without noise or with little noise is produced by using AutoCAD software; RGB two-dimensional color image information is used as a training image input, the training image is rotated at different angles and scaled at different scales to obtain training templates of training images at different angles and scales; the training template is initialized, and Gaussian blur processing is performed to smooth the image and reduce noise interference; the image edge is calculated by using the Sobel operator, the Sobel operator is applied to the blurred image, and the gradients in the horizontal and vertical directions are calculated; the gradient direction is calculated for vector space projection, the gradient amplitude is screened according to a preset threshold to obtain a gradient histogram, and the feature vector is obtained according to the gradient histogram; the feature point coordinates are determined according to the feature vector of the feature point; and the feature vector and the feature point coordinates are saved to a matching template image.

[0039] Template matching is performed through the LINE-MOD algorithm to adjust the width and height of the image to be detected; the feature vector of the image to be detected is calculated, and the coordinate position of the feature point is calculated through the image pyramid; the image to be detected is expanded in the gradient direction, and a gradient response map is constructed to linearly store the gradient response.

[0040] Extract training template information, traverse the template image pyramid, extract the corresponding template image and perform horizontal and vertical sliding window matching with the image to be detected, and calculate the feature point similarity based on the gradient difference; accumulate the feature point similarities to calculate the matching degree, and perform template matching based on the matching degree.

[0041] The needle mark in the Pad area is judged; when there is a needle mark, the needle mark edge touching rule is judged, and when the needle mark meets the needle mark edge touching rule, it is judged as failing the detection; when the needle mark does not meet the needle mark edge touching rule, it is judged as passing the detection; when there is no needle mark, it is judged as passing the detection.

[0042] It is not difficult to find that compared with the related technology, the solution provided in the embodiment of the present application adopts the LINE-MOD template matching algorithm to output the pad area. The LINE-MOD algorithm supports sub-pixels and has higher accuracy. At the same time, compared with the deep learning algorithm, the LINE-MOD algorithm does not rely on large-scale data annotation and is suitable for industrial scenarios where data collection is difficult.

[0043] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0044] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0045] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 5 An exemplary structural diagram of the electronic device is disclosed. Figure 5 As shown, the electronic device includes: one or more processors 1101, a memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0046] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0047] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator rod, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0048] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0049] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0050] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0051] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0052] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0053] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0054] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0055] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. For example, an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device may be used to implement the embodiments. In some embodiments, the software program of the present application may be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) may be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present application may be implemented by hardware, for example, as a circuit that cooperates with a processor to perform various steps or functions.

[0056] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center to another website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0057] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0058] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0059] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for detecting a needle mark on a chip pad, characterized in that: The method comprises: Collect and create template images; Acquire a training picture, process the training picture by using the LINE-MOD algorithm to obtain a feature vector and feature point coordinates of the training picture; save the feature vector and feature point coordinates to the corresponding template picture; Acquire a picture to be detected, match the picture to be detected with the template picture through the LINE-MOD algorithm, and output a detection picture; the detection picture includes the matched Pad information, position and confidence; The detection image is processed by an adaptive threshold segmentation algorithm; the needle mark area of ​​the detection image is segmented, and the size spacing between the needle mark area and the pad frame is calculated, the needle mark in the pad area is judged according to the size spacing, and the detection result is output; When there is a needle mark, a needle mark edge touching rule is judged. When the needle mark meets the needle mark edge touching rule, it is judged as failing the test; when the needle mark does not meet the needle mark edge touching rule, it is judged as passing the test; When there is no needle mark, it is determined to have passed the test.

2. The method according to claim 1, characterized in that The processing of the training image by using the LINE-MOD algorithm includes: The training image is subjected to Gaussian blur processing, the image edge is calculated by the Sobel operator, the gradient direction is calculated for vector space projection, the gradient amplitude is screened according to a preset threshold to obtain a gradient histogram, and the feature vector is obtained according to the gradient histogram.

3. The method according to claim 2, characterized in that The processing of the training image by using the LINE-MOD algorithm includes: The training images are rotated in angle and scaled in size, and training is performed based on training images of different angles and different sizes.

4. The method according to claim 3, characterized in that The matching of the to-be-detected picture with the template picture by using the LINE-MOD algorithm includes: Calculate the feature vector and feature point coordinates of the image to be detected by LINE-MOD algorithm; expand the image to be detected according to the gradient direction to construct a gradient response map; The gradient response map is matched with the template image by horizontal and vertical sliding windows; the feature point similarity is calculated according to the gradient difference; the feature point similarities are accumulated to calculate the matching degree, and template matching is performed according to the matching degree.

5. The method according to any one of claims 1 to 4, characterized in that: The collecting and making of template images includes: making template images without noise or with little noise by using AutoCAD.

6. The method according to claim 1, characterized in that The training picture and the picture to be detected include image RGB information.

7. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 6.

8. A computer readable medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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