Target object edge debris detection method and device based on image histogram, medium and product

Abstract

The embodiment of the invention relates to the technical field of image processing, and discloses a target object edge debris detection method and device based on an image histogram, a medium and a product. The method comprises the following steps: acquiring an image of a target object, and obtaining a central position of the target object in the image; determining at least two detection areas of the target object according to the central position; acquiring an image histogram of each detection area; and analyzing the image histograms of the detection areas, and determining whether chippings exist on the edge of the target object according to an analysis result. By adopting the scheme, whether chippings exist at the edge of the target object can be accurately identified based on the image histogram, and the chipping identification accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method, device, medium and product for detecting edge debris of a target object based on an image histogram. Background Art

[0002] In recent years, with the rapid development of science and technology, the fields of mechanical manufacturing and processing have developed rapidly.

[0003] In the visual inspection equipment added during the on-site machine tool transformation, the robot clamps the target object to be processed from the loading station and places it in the machine tool processing station. After the processing is completed, the camera carried by the robot takes a photo of the processed alien wheel, and the robot takes the next step based on the results of the photo. If there are iron filings or other debris on the target object, the system will display an alarm. If there are no iron filings or other debris on the target object, the robot will clamp the normal product and place it in the discharge area. The traditional visual inspection method locates the center position of the target product through template matching, takes the center position of the product as the origin, establishes the ROI (region of interest) for product edge finding, finds the boundary line of the product through each ROI, and finally determines whether the distance and angle of each boundary line are within the normal range. If not, it is determined that there are iron filings or other debris.

[0004] However, in the above detection method, many misjudgments will occur. For example, misjudgment may occur due to product mismatch, and misjudgment may also occur due to errors in edge finding, resulting in low accuracy in debris identification. Summary of the invention

[0005] One purpose of the present application is to provide a method, device, medium and product for detecting debris at the edge of a target object based on an image histogram, at least to solve the problem of low debris recognition accuracy. The present application collects an image of a target object and obtains the center position of the target object in the image; determines at least two detection areas of the target object based on the center position; obtains an image histogram of each detection area; analyzes the image histogram of each detection area, and determines whether there is debris at the edge of the target object based on the analysis results. By adopting this solution, it is possible to accurately identify whether there is debris at the edge of the target object based on the image histogram, thereby improving the accuracy of debris recognition.

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

[0007] In a first aspect, some embodiments of the present application provide a method for detecting edge debris of a target object based on an image histogram, the method comprising:

[0008] Collect an image of the target object and obtain the center position of the target object in the image;

[0009] Determining at least two detection areas of the target object according to the center position;

[0010] Obtaining the image histogram of each detection area;

[0011] The image histogram of each detection area is analyzed, and whether there are debris at the edge of the target object is determined according to the analysis result.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] Compared with the related art, the solution provided in the embodiment of the present application acquires an image of the target object and obtains the center position of the target object in the image; determines at least two detection areas of the target object according to the center position; obtains the image histogram of each detection area; analyzes the image histogram of each detection area, and determines whether there is debris at the edge of the target object according to the analysis result. By adopting this solution, whether there is debris at the edge of the target object can be accurately identified based on the image histogram, thereby improving the accuracy of debris identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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.

[0017] Figure 1 An exemplary flow chart of a target object edge debris detection method based on an image histogram according to some embodiments of the present application;

[0018] Figure 2 A schematic diagram of a detection area division result provided according to some embodiments of the present application;

[0019] Figure 3 A comparative schematic diagram of a device with and without debris provided according to some embodiments of the present application;

[0020] Figure 4 A schematic diagram of a debris-free image histogram provided according to some embodiments of the present application;

[0021] Figure 5 A schematic diagram of a histogram of an image with debris provided according to some embodiments of the present application;

[0022] Figure 6 An exemplary structural diagram of the electronic device is disclosed. DETAILED DESCRIPTION

[0023] 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.

[0024] First embodiment

[0025] The first embodiment of the present application relates to a method for detecting edge debris of a target object based on an image histogram. Figure 1 As shown, the method may include the following steps:

[0026] Step S101, capturing an image of a target object and obtaining the center position of the target object in the image;

[0027] The target object may be various processed parts, for example, a metal processed part clamped by a robot.

[0028] In one embodiment, the target object is an extraterrestrial planet.

[0029] Among them, the outer star wheel generally has a special contour shape, which is often similar to a disc-shaped structure with a pulley tooth or a special curved surface. The tooth shape or curved surface contour of its outer edge is often carefully designed according to the specific transmission function requirements. For example, some outer star wheel teeth are involute shaped, which are used to achieve precise meshing transmission with other matching parts. The overall size will vary greatly depending on the application scenario. The small one may be only a few centimeters in diameter, and the large one can reach several meters for large mechanical equipment.

[0030] In one embodiment, capturing an image of a target object and obtaining a center position of the target object in the image includes:

[0031] An image of a target object is captured, and the center position of the target object in the image is located by template matching.

[0032] Template matching is a target location method based on image pixel features. Its core idea is to slide a small image (i.e., template image) prepared in advance and representing the target object in the large image to be detected (original image) according to certain rules, and calculate the similarity between the template image and the corresponding position area of ​​the original image. The higher the similarity, the more likely it is that the target object is located.

[0033] Get the original image: an overall image of the target object to be located, such as a production line monitoring image containing a specific part, a scene photo with a specific sign, etc.

[0034] Determine the template image: A small image captured from a known target object image should be able to accurately reflect the characteristics of the target object. The size is usually much smaller than the original image. For example, a product trademark pattern is captured as a template to locate the trademark in the original advertising poster image.

[0035] Starting from the upper left corner of the original image, slide and translate the template image row by row and column by pixel (or a set step size, such as every few pixels). At each stop position, use a specific similarity calculation method (such as the commonly used square difference matching, normalized correlation coefficient matching, etc.) to measure the similarity between the template image and the current coverage area of ​​the original image. Finally, a matching result matrix related to the size of the original image is obtained. Each element value in the matrix represents the similarity of the corresponding position. The larger the value (according to the characteristics of different calculation methods), the higher the similarity and the closer it is to the target object.

[0036] In the obtained matching result matrix, find the element position with the highest similarity. This position corresponds to the position where the template image has the best match in the original image. It usually represents the upper left corner of the target object (from the perspective of template sliding).

[0037] Once the coordinates of the upper left corner of the target object are determined, combined with the known width and height of the template image (these two dimensions are fixed and can be obtained in advance), the center coordinates of the target object in the original image can be calculated through simple mathematical operations (the upper left corner horizontal coordinate plus half of the template width is the center horizontal coordinate, and the upper left corner vertical coordinate plus half of the template height is the center vertical coordinate), thereby realizing the positioning of the center position of the target object.

[0038] Through such a setting, this scheme can improve the positioning accuracy of the center position of the target object and provide a data basis for subsequent calculations.

[0039] Step S102, determining at least two detection areas of the target object according to the center position;

[0040] Figure 2 Schematic diagram of the detection area division result provided according to some embodiments of the present application. Figure 2 As shown, the target object can be divided into multiple detection areas according to the central position of the target object and the shape of the target object.

[0041] In one embodiment, determining at least two detection areas of the target object according to the center position includes:

[0042] The contour position of the target object is determined according to the center position, and the target object is segmented to obtain at least two detection areas.

[0043] Determine the contour position based on the center position, which can be based on the threshold segmentation method (taking grayscale image as an example). First, convert the original image containing the target object into a grayscale image (if it is not a grayscale image itself). For example, in Python, using the OpenCV library, this can be achieved through cv2.cvtColor(image,cv2.COLOR_BGR2GRAY), where image is the original color image.

[0044] Then, take the center of the target object as the reference point, select a suitable threshold segmentation algorithm (such as the commonly used Otsu threshold method cv2.threshold() with cv2.TM_CCOEFF_NORMED threshold type), and gradually expand from the center to the surrounding area to distinguish the target object pixels and background pixels, and construct the rough outline of the target object. The Otsu threshold method will automatically calculate an optimal threshold so that the inter-class variance of the segmented target and background is the largest, thereby more accurately separating the pixels in the target object area.

[0045] Since we already know the center position, we can set a suitable search radius or search range (such as a circular area with a certain pixel length as the radius and the center position as the center, or a rectangular area with the center position as the center, etc.), and apply threshold segmentation operations within this limited range to improve efficiency and reduce interference from irrelevant background, and finally determine the set of contour pixels of the target object.

[0046] Based on the method of edge detection combined with center position, the image is also preprocessed as necessary (such as grayscale, and if possible, Gaussian filtering to remove noise, for example, cv2.GaussianBlur(gray_image,(5,5),0), where gray_image is the grayscale image).

[0047] Use edge detection algorithms, such as the classic Canny edge detection algorithm cv2.Canny(), to detect from the center to the periphery. The Canny algorithm determines the edge by finding places in the image where the pixel intensity changes dramatically. It is based on principles such as gradient calculation. It first calculates the gradient amplitude and direction of the image, and then uses double threshold processing to screen out the true edge pixels.

[0048] Using the center position as a starting guide, the detected edge pixel points are connected to form a closed or approximately closed curve to outline the contour of the target object. For example, some contour tracking algorithms can be used to start from the edge points near the center and gradually track and connect adjacent edge points along the gradient direction until returning to the starting point or forming a qualified contour boundary.

[0049] The target object is segmented to obtain the detection area. Regular shape segmentation can be used, and horizontal or vertical segmentation can be used: after determining the contour of the target object, if the shape of the target object is relatively regular, such as a rectangular shape, the target object can be simply divided into at least two detection areas in the horizontal or vertical direction according to the pre-set rules. For example, if the contour range of the target object is a rectangular area with a width of W and a height of H, if it is to be segmented into two parts horizontally, a horizontal segmentation line can be drawn at the position of H / 2 in the height direction by calculation to divide the target object into two upper and lower detection areas; similarly, for vertical segmentation, a vertical segmentation line is drawn at the position of W / 2 in the width direction to divide the area.

[0050] Grid segmentation: Imagine the contour range of the target object as a large rectangular area (even if the object itself is not a strict rectangle, it can be approximated by its circumscribed rectangle), and then divide it into multiple small grid-shaped detection areas according to the row and column settings. For example, if you want to divide it into 3 rows and 3 columns with a total of 9 detection areas, then divide it into 3 equal parts according to the width and height of the target object's contour. These small detection areas are constructed by drawing horizontal and vertical dividing lines. It is often used in scenarios that require more detailed and uniform local detection of target objects, such as detecting texture features at different locations on the surface of an object.

[0051] Irregular segmentation based on features, such as segmentation based on texture features. When there are different texture patterns on the surface of the target object, a texture analysis algorithm is used (such as grayscale co-occurrence matrix, local binary pattern and other texture descriptors to analyze texture feature differences) to determine the boundary of texture changes. This is used as the basis for segmentation to divide the target object into different detection areas. It is often used to detect the surface quality of objects with patterns or changes in patterns or to identify different parts of an object.

[0052] Through the above series of operations, the contour position of the target object can be effectively determined according to its center position, and it can be reasonably divided to obtain multiple areas that meet different detection needs, laying the foundation for subsequent more in-depth and detailed detection, analysis and identification of the target object.

[0053] Step S103, obtaining an image histogram of each detection area;

[0054] Histogram is a method of statistically analyzing data and placing the statistical values ​​into a series of well-defined bins. Among them, bin is a concept often used in histograms, which can be translated as "straight bar" or "group distance". Its value is a characteristic statistic calculated from the data, which can be gradient, direction, color or any other feature.

[0055] Image Histogram is a histogram used to represent the brightness distribution in a digital image, plotting the number of pixels for each brightness value in the image. In this histogram, the left side of the horizontal axis is the darker area, while the right side is the brighter area. Therefore, the data in the histogram of a darker image is mostly concentrated on the left and middle parts, while the opposite is true for an image that is bright overall with only a few shadows.

[0056] Step S104: analyzing the image histogram of each detection area, and determining whether there are debris at the edge of the target object according to the analysis result.

[0057] Figure 3 Schematic diagram of comparison between debris and no debris provided according to some embodiments of the present application. Figure 3 As shown, around the outer star wheel workpiece, there is no debris as in the left half, and there is debris as in the right half.

[0058] It can be understood that for different situations, the obtained image histogram is different.

[0059] In one embodiment, analyzing the image histogram of each detection area and determining whether there is debris at the edge of the target object according to the analysis result includes:

[0060] The grayscale distribution analysis is performed on the image histogram of each detection area. If the grayscale distribution does not match the theoretical distribution, it is determined that debris exists at the edge of the target object.

[0061] In this solution, the grayscale distribution of the image histogram can be analyzed to determine whether there are debris on the edge of the target object. The image histogram is a graph that reflects the relationship between each grayscale level and the frequency of occurrence of each grayscale pixel in an image. The horizontal axis represents the grayscale value of each pixel in the image, which usually ranges from 0 (black) to 255 (white); the vertical axis represents the number of pixels with this grayscale value. By analyzing the histogram, the grayscale distribution of the image can be intuitively understood. If the pixels of the histogram are mainly concentrated in the low grayscale value area, it means that the image may be darker; if they are concentrated in the high grayscale value area, the image may be brighter; if the distribution is more uniform, the grayscale level of the image is richer. The width of the histogram can reflect the contrast of the image. If the grayscale range covered by the histogram is wider, it means that the image has a higher contrast; otherwise, the contrast is lower. The peaks in the histogram represent the grayscale values ​​that appear more frequently in the image. These peaks may correspond to the main objects or areas in the image. The valleys represent the transition area where the grayscale value changes, which may be the boundary of the object or the junction of different areas. The image can be further analyzed by calculating some statistics of the histogram, such as the mean, which represents the average brightness of the image, and the variance, which reflects the degree of dispersion of the grayscale values ​​of the image. A higher mean indicates that the image is brighter overall, while a larger variance indicates that the grayscale values ​​of the image vary greatly, and may have more details and textures.

[0062] In summary, histogram analysis in image processing provides an important basis for understanding the characteristics of images. According to the principle of histogram analysis of images, it can be applied to iron chip detection in the processing of alien wheels.

[0063] In one embodiment, grayscale distribution analysis is performed on the image histogram of each detection area, and if the grayscale distribution does not match the theoretical distribution, it is determined that debris exists at the edge of the target object, including:

[0064] Identify the image histogram of each detection area and perform grayscale distribution curve;

[0065] If there is a difference between the grayscale distribution curve of at least one detection area and the theoretical distribution curve, it is determined that there are debris at the edge of the target object in the current detection area.

[0066] In this solution, the grayscale distribution curve can be identified. Specifically, when it is identified that the fluctuation of the grayscale distribution curve is different from the fluctuation of the theoretical distribution curve, it is determined that debris exists.

[0067] The theoretical distribution curve may be obtained by using the same partitioning method to obtain the image histogram of each partition after the image is collected in advance in a scene where there is clearly no debris, thereby obtaining the theoretical grayscale distribution and the theoretical distribution curve.

[0068] This solution can identify whether there are debris in the workpiece from the perspective of the curve, thereby improving the recognition accuracy.

[0069] In one embodiment, grayscale distribution analysis is performed on the image histogram of each detection area, and if the grayscale distribution does not match the theoretical distribution, it is determined that debris exists at the edge of the target object, including:

[0070] The grayscale interval distribution ratio of the image histogram of each detection area is calculated. If the grayscale interval distribution ratio of at least one detection area is different from the theoretical grayscale interval distribution ratio, it is determined that there are debris at the edge of the target object in the current detection area.

[0071] As another way, it is also possible to identify whether there are differences from the distribution ratio of the image histogram in each grayscale interval in each detection area. For example, in theory, the grayscale interval distribution ratio is uniform, but in the presence of debris, it is found that the distribution becomes less in the low grayscale interval and more in the high grayscale interval. Therefore, it can be determined that the presence of debris causes abnormality.

[0072] Figure 4 A schematic diagram of a debris-free image histogram provided according to some embodiments of the present application. Figure 5 Schematic diagram of a histogram of an image with debris provided according to some embodiments of the present application. Figure 4 and Figure 5 As shown, in the presence of debris, there will be obvious fluctuations in the image histogram, and the number of grayscale intervals with higher brightness is greater.

[0073] This solution can quickly identify defects through such an identification method. The image histogram can quickly determine the abnormal or defective areas in the image by analyzing the distribution of pixel brightness (grayscale) in the image. Improve detection accuracy. Combined with threshold processing technology, the image histogram can convert the image into a binary image, so as to more clearly display defects, such as surface cracks or defects. This method is simple and efficient and is suitable for many types of defect detection. Reduce false alarm and false negative rates. The histogram analysis method based on deep learning can effectively locate and identify defective areas in complex backgrounds, further reducing false alarm and false negative rates. For example, the histogram analysis method combined with the convolutional neural network (CNN) can effectively identify defects such as bubbles and scratches in complex automotive industry paint defect detection.

[0074] In addition, this scheme has low computational cost, the calculation of image histogram is relatively simple, and it has the advantages of no deformation when the image is translated, rotated, or scaled. This makes it widely used in real-time and resource-constrained environments. Strong adaptability: the image histogram method can adapt to the needs of defect detection of different sizes and shapes. Through multi-scale and multi-channel histogram analysis methods, it can more comprehensively capture the diversity and complexity of images. Strong robustness: combined with traditional methods such as Gaussian models, image histograms can effectively deal with noise interference and improve the robustness of segmentation algorithms. Widely used: image histograms not only perform well in industrial defect detection, but are also widely used in image segmentation, image retrieval, and image classification. For example, in many industries such as electronics manufacturing, automotive industry, and food processing, image histogram methods have been successfully applied to various defect detection tasks.

[0075] In general, using image histograms for workpiece debris detection can not only quickly and accurately identify abnormal or defective areas in the image, but also improve detection accuracy and reduce false alarm and missed alarm rates. Its low computational cost, strong adaptability, high robustness and wide application areas make it an indispensable and important tool in industrial production lines.

[0076] The technical solution provided in this embodiment collects an image of a target object and obtains the center position of the target object in the image; determines at least two detection areas of the target object according to the center position; obtains an image histogram of each detection area; analyzes the image histogram of each detection area, and determines whether there is debris at the edge of the target object according to the analysis result. By adopting this solution, whether there is debris at the edge of the target object can be accurately identified based on the image histogram, thereby improving the accuracy of debris identification.

[0077] 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.

[0078] 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 6 An exemplary structural diagram of the electronic device is disclosed. Figure 6As shown, the electronic device includes: one or more processors 601, memory 602, 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 together 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 used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0079] The electronic device may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0080] The input device 603 can receive input digital or character information, and generate 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 bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 604 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.

[0081] 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 the 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).

[0082] 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.

[0083] The memory 602 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 601 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 602, so as to implement the program instructions / modules corresponding to the method provided by any one or more of the above embodiments in the embodiments of the present application.

[0084] The memory 602 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 602 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 devices. In some embodiments, the memory 602 may optionally include a memory remotely arranged relative to the processor 601, 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.

[0085] It should be noted that the computer-readable medium described in the present 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, Random AccElasticsearchs Memory), a read-only memory (ROM, Read-Only Memory), 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 the present application, a computer-readable medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0086] 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 medium that can be used to store information that can be accessed by a computing device.

[0087] 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 cases involving 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).

[0088] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0089] 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 a 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 by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. 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.

[0090] 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.

[0091] 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.

[0092] 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 edge debris of a target object based on an image histogram, characterized in that: The method comprises: Collect an image of the target object and obtain the center position of the target object in the image; Determining at least two detection areas of the target object according to the center position; Obtaining the image histogram of each detection area; The image histogram of each detection area is analyzed, and whether there are debris at the edge of the target object is determined according to the analysis result.

2. The method according to claim 1, characterized in that Analyzing the image histogram of each detection area, and determining whether there is debris at the edge of the target object according to the analysis result, including: The grayscale distribution analysis is performed on the image histogram of each detection area. If the grayscale distribution does not match the theoretical distribution, it is determined that debris exists at the edge of the target object.

3. The method according to claim 2, characterized in that The grayscale distribution analysis is performed on the image histogram of each detection area. If the grayscale distribution does not match the theoretical distribution, it is determined that there are debris at the edge of the target object, including: Identify the image histogram of each detection area and perform grayscale distribution curve; If there is a difference between the grayscale distribution curve of at least one detection area and the theoretical distribution curve, it is determined that there are debris at the edge of the target object in the current detection area.

4. The method according to claim 2, characterized in that: The grayscale distribution analysis is performed on the image histogram of each detection area. If the grayscale distribution does not match the theoretical distribution, it is determined that there are debris at the edge of the target object, including: The grayscale interval distribution ratio of the image histogram of each detection area is calculated. If the grayscale interval distribution ratio of at least one detection area is different from the theoretical grayscale interval distribution ratio, it is determined that there are debris at the edge of the target object in the current detection area.

5. The method according to claim 1, characterized in that Collect an image of the target object and obtain the center position of the target object in the image, including: An image of a target object is captured, and the center position of the target object in the image is located by template matching.

6. The method according to claim 1, characterized in that Determining at least two detection areas of the target object according to the center position includes: The contour position of the target object is determined according to the center position, and the target object is segmented to obtain at least two detection areas.

7. The method according to claim 1, characterized in that The target object is an extraterrestrial wheel.

8. 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 7.

9. A computer readable medium having a computer program / instructions 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 7 are implemented.

10. 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 7 are implemented.

Images