Interior trim part defect detection method and system, computer and storage medium

Abstract

The invention provides an interior trim part defect detection method and system, a computer and a storage medium. The method comprises the following steps: acquiring image data of a vehicle interior trim part; edge features of the front view image are extracted, and detection coordinates corresponding to the welding area are obtained; selecting a target sub-image from the front view image based on the detection coordinates, calculating an average gray value corresponding to each pixel in the target sub-image, and judging whether the average gray value exceeds a preset range; if the average gray value exceeds the preset range, it is judged that the interior trim part has the welding defect. Whether welding appearance abnormity exists or not is judged by calculating the average gray value of the target sub-image; the feature extraction is further performed on the abnormal image through the SURF algorithm, and then the feature is matched with the template features of different defect types through the RANSAC algorithm to identify the defect types, so that compared with a global image feature extraction detection mode, the detection efficiency can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of interior decoration part detection, and in particular to an interior decoration part defect detection method, system, computer and storage medium. Background Art

[0002] In order to enhance the strength and stability of interior parts such as door trim panels and instrument panels, horizontal and vertical metal strips are welded together. Whether the welding is good or not is related to the installation accuracy and appearance quality of the interior parts.

[0003] In the existing technology, welding quality inspection usually requires manual inspection to observe and inspect whether there are defects such as cracks, incomplete penetration, pores, weld bumps, etc. on the surface; or through an optical inspection system, images are collected to determine whether there are defects based on global recognition inspection of image features, and the inspection efficiency is low. Summary of the invention

[0004] In view of the deficiencies in the prior art, the object of the present invention is to provide a method, system, computer and storage medium for detecting defects in interior decoration parts, aiming to solve the technical problem of low detection efficiency in the prior art.

[0005] In order to achieve the above object, in a first aspect, the present invention provides: a method for detecting defects in interior decoration parts, comprising the following steps: Acquire image data of a vehicle interior trim, and perform perspective transformation processing on the image data to obtain a front view image; Extracting edge features of the front view image, acquiring center coordinates corresponding to the interior decoration part based on the edge features, and obtaining detection coordinates corresponding to the welding area based on the center coordinates and the edge features; Selecting a target sub-image from the front view image based on the detection coordinates, calculating an average grayscale value corresponding to each pixel in the target sub-image, and determining whether the average grayscale value exceeds a preset range; If the average gray value exceeds a preset range, it is determined that the interior trim has a welding defect.

[0006] According to one aspect of the above technical solution, after the step of determining whether the vehicle interior trim has a welding defect, the method further includes: Obtain the grayscale value of the target sub-image corresponding to each welding area, select the corresponding target sub-image whose grayscale value exceeds the preset range, and mark it as an abnormal image; Extract features of the abnormal image based on the SURF algorithm to obtain defect feature points; The defect feature point is matched with the matching feature point in the template image, and the optimal matching point is selected based on the Euclidean distance between the matching feature point and the defect feature point, so as to obtain the defect type corresponding to the abnormal image.

[0007] According to one aspect of the above technical solution, the step of selecting the optimal matching point based on the Euclidean distance between the matching feature point and the defect feature point to obtain the defect type corresponding to the abnormal image specifically includes: The Euclidean distance between each matching feature point and the defect feature point is calculated, sorted based on the size of each Euclidean distance, and some defect feature points whose Euclidean distance is greater than a preset threshold are screened out to obtain a target feature point set; The RANSAC algorithm is used to perform internal point determination based on the target feature point set to match the corresponding template image, thereby obtaining the defect type corresponding to the abnormal image.

[0008] According to one aspect of the above technical solution, the expression of the Euclidean distance corresponding to the target feature point set is as follows: ; Where d is the Euclidean distance corresponding to the target point set, and are respectively the maximum and minimum values ​​of the Euclidean distances between each of the matching feature points and the defect feature points, and k is a proportional coefficient.

[0009] According to one aspect of the above technical solution, before the step of calculating the average grayscale value corresponding to each pixel in the target sub-image, the method further includes: The target sub-image is filtered by low-pass filtering to obtain the average value of the grayscale value, so as to obtain the grayscale value of the target sub-image after processing based on the following calculation formula: ; In the formula, is the gray value of the target sub-image after processing, is the gray value of the target sub-image before processing, represents the rounding function, v is the grayscale median, is the grayscale mean, and F is the crossover frequency.

[0010] According to one aspect of the above technical solution, the step of selecting a target sub-image from the front view image based on the detection coordinates specifically includes: Selecting a weld center point corresponding to the weld shape from the front view image based on the detection coordinates; The pixel points on the center point of the weld are used as an initial seed point set, and a region growing method is used to segment the front view image to form a target sub-image based on the grayscale difference value between the initial seed point set and the neighboring pixel points.

[0011] According to one aspect of the above technical solution, the growth condition expression corresponding to the grayscale difference value is: ; ; ; ; ; In the formula, is the weighted gray value of the pixel point in the area to be grown, is the mean gray value of the pixels in the grown area, is the adaptive threshold of grayscale change, is the preset range, n is the number of pixels in the grown area, is the gray value variance of the pixels in the grown area, is the first gray value of the weld center point, are the coordinates of the weld center point, is the second grayscale value of the neighborhood pixel, is the coordinate of the neighborhood pixel, j is the number of pixels in the field, is the third grayscale value of the surrounding pixels of the neighborhood pixel, is the coordinates of the pixels around the neighborhood pixel, k is the number of pixels around the neighborhood pixel, , , are the weight coefficients of the first grayscale value, the second grayscale value and the third grayscale value respectively.

[0012] In a second aspect, the present invention provides an interior decoration defect detection system, comprising: An image module, used to obtain image data of vehicle interior parts, and perform perspective transformation processing on the image data to obtain a front view image; An edge feature module, used to extract edge features of the front view image, obtain the center coordinates corresponding to the interior decoration part based on the edge features, and obtain the detection coordinates corresponding to the welding area based on the center coordinates and the edge features; A grayscale module, used to select a target sub-image from the front view image based on the detection coordinates, calculate an average grayscale value corresponding to each pixel in the target sub-image, and determine whether the average grayscale value exceeds a preset range; The determination module is used to determine whether the interior decoration part has a welding defect if the average gray value exceeds a preset range.

[0013] According to one aspect of the above technical solution, the system further includes: The defect type module is used to obtain the grayscale value of the target sub-image corresponding to each welding area, select the corresponding target sub-image whose grayscale value exceeds the preset range, and mark it as an abnormal image; Extract features of the abnormal image based on the SURF algorithm to obtain defect feature points; The defect feature point is matched with the matching feature point in the template image, and the optimal matching point is selected based on the Euclidean distance between the matching feature point and the defect feature point, so as to obtain the defect type corresponding to the abnormal image.

[0014] According to one aspect of the above technical solution, the defect type module is specifically used for: The Euclidean distance between each matching feature point and the defect feature point is calculated, sorted based on the size of each Euclidean distance, and some defect feature points whose Euclidean distance is greater than a preset threshold are screened out to obtain a target feature point set; The RANSAC algorithm is used to perform internal point determination based on the target feature point set to match the corresponding template image, thereby obtaining the defect type corresponding to the abnormal image.

[0015] According to one aspect of the above technical solution, the grayscale module is specifically used for: Selecting a weld center point corresponding to the weld shape from the front view image based on the detection coordinates; The pixel points on the center point of the weld are used as an initial seed point set, and a region growing method is used to segment the front view image to form a target sub-image based on the grayscale difference value between the initial seed point set and the neighboring pixel points.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: by acquiring image data of vehicle interior parts, and obtaining detection coordinates corresponding to the welding area of ​​the interior parts according to the edge features of the interior parts, a target sub-image is selected from the image data, and by calculating the average grayscale value of the target sub-image, it is determined whether there is any abnormal welding appearance; further, the abnormal image is feature extracted by the SURF algorithm, and then the features are matched with template features of different defect types by the RANSAC algorithm to identify the defect type, which can effectively improve the detection efficiency compared with the global image feature extraction detection method; since the distances between the correct matching points of the weld are basically the same, some defect feature points whose Euclidean distance is greater than the preset threshold are screened out, which can reduce the amount of calculation and achieve the purpose of accurate matching; by filtering the target sub-image with a low-pass filter, the influence of image illumination can be reduced; by segmenting the target sub-image with the regional growing method, the pixel points at the center point of the weld are used as the initial seed point set, and the adaptive threshold of the grayscale change is set according to the set growth conditions, the weld image can be effectively extracted, and the accuracy of subsequent defect type recognition can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1It is a flow chart of the method for detecting defects of interior decoration parts in the first embodiment of the present invention; Figure 2 It is a structural block diagram of the interior decoration defect detection system in the second embodiment of the present invention; Figure 3 is a schematic diagram of the hardware structure of a computer in a third embodiment of the present invention; The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0018] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0021] Embodiment 1 See also Figure 1 , which is a flow chart of a method for detecting defects in interior decoration parts in a first embodiment of the present invention. As shown in the figure, the method includes the following steps: Step S100, acquiring image data of vehicle interior trim, performing perspective transformation processing on the image data, and obtaining a front view image.

[0022] Specifically, in this embodiment, the above-mentioned vehicle interior part is the dashboard frame, which is the main supporting structure of the dashboard and is composed of multiple metal parts. Most of the connections between these parts are completed by welding, such as the connection between the crossbeam and the longitudinal beam. Stable welding can ensure that the dashboard frame has sufficient strength to bear the weight of components such as instruments, air outlets, and control panels; the above-mentioned image data is the image data after welding processing, and the angle difference between the acquisition plane and the workpiece plane is eliminated through perspective transformation processing.

[0023] Step S200, extract edge features of the front view image, obtain the center coordinates of the interior trim based on the edge features, and obtain the detection coordinates of the welding area based on the center coordinates and the edge features. The edge features can be extracted using the Canny edge detection algorithm, the gradient operator method, etc.

[0024] Step S300 , selecting a target sub-image from the front view image based on the detection coordinates, calculating an average grayscale value corresponding to each pixel in the target sub-image, and determining whether the average grayscale value exceeds a preset range.

[0025] It can be understood that the above-mentioned target sub-image corresponds to the image of the weld. Since the distribution pattern of the welds is the same, if there is no problem with the welding, the average grayscale value of each pixel in the image is within a certain range; if the average grayscale value is too high or too low, it means that there are abnormalities such as welding leaks and weld nodules in the welding. The grayscale value inspection method can improve the detection efficiency.

[0026] Preferably, in the above step S300, before the step of calculating the average grayscale value corresponding to each pixel in the target sub-image, the method further includes: The target sub-image is filtered by low-pass filtering to obtain the average value of the grayscale value, so as to obtain the grayscale value of the target sub-image after processing based on the following calculation formula: ; In the formula, is the gray value of the target sub-image after processing, is the gray value of the target sub-image before processing, represents the rounding function, v is the grayscale median, is the grayscale mean, and F is the crossover frequency. By low-pass filtering the target sub-image, the part with large grayscale value in the image becomes smaller, and the part with small grayscale value becomes larger, so as to achieve the purpose of balancing the lighting and reduce the impact of light on the image.

[0027] Preferably, in the above step S300, the step of selecting a target sub-image from the front view image based on the detection coordinates specifically includes: Step S310, selecting a weld center point corresponding to the weld shape from the front view image based on the detection coordinates.

[0028] Step S320, using the pixel points on the center point of the weld as an initial seed point set, and using a region growing method to segment the front view image to form a target sub-image based on the grayscale difference between the initial seed point set and the neighboring pixel points.

[0029] It can be understood that compared with the method of obtaining the target sub-image by selecting a rectangular box through detection coordinates, the target sub-image corresponding to the weld image can be more accurately segmented by the region growing method. At the same time, the center point of the weld, that is, the pixel point in the middle of the weld image, is used as the initial seed point, which can improve the growth efficiency and accuracy.

[0030] Furthermore, in this embodiment, the growth condition expression corresponding to the grayscale difference value is: ; ; ; ; ; In the formula, is the weighted gray value of the pixel point in the area to be grown, is the mean gray value of the pixels in the grown area, is the adaptive threshold of grayscale change, is the preset range, n is the number of pixels in the grown area, is the gray value variance of the pixels in the grown area, is the first gray value of the weld center point, are the coordinates of the weld center point, is the second grayscale value of the neighborhood pixel, is the coordinate of the neighborhood pixel, j is the number of pixels in the field, is the third grayscale value of the surrounding pixels of the neighborhood pixel, is the coordinates of the pixels around the neighborhood pixel, k is the number of pixels around the neighborhood pixel, , , are the weight coefficients of the first grayscale value, the second grayscale value and the third grayscale value respectively.

[0031] Specifically, in this embodiment, the neighborhood pixel point, i.e., the eight pixels around the nine-square grid pixel point of the center point of the weld, and the surrounding pixels of the above-mentioned neighborhood pixel point, i.e., the four pixels above, below, left and right outside the above-mentioned eight pixels, are taken as the grayscale mean in the twelve neighborhoods around the growth point, and the threshold is adaptively changed at the same time. The adaptive ability of regional growth can be enhanced, the over-segmentation, under-segmentation and incomplete growth phenomena can be improved, and the robustness can be improved.

[0032] Step S400: If the average gray value exceeds a preset range, it is determined that there is a welding defect in the interior decoration part.

[0033] It can be understood that if the above average grayscale value is within the preset range, it means that the brightness corresponding to the target sub-image is similar to that of the standard image, which means that the welding appearance is normal.

[0034] Preferably, in this embodiment, after the step of determining whether the vehicle interior trim part has a welding defect, the method further comprises: Step S500, obtaining the grayscale value of the target sub-image corresponding to each welding area, selecting the corresponding target sub-image whose grayscale value exceeds a preset range, and marking it as an abnormal image.

[0035] Step S600, extracting features from the abnormal image based on the SURF algorithm to obtain defect feature points. The SURF algorithm is the Speeded Up Robust Features algorithm, which is a feature detection and description algorithm used for image recognition and computer vision. The SURF algorithm can stably detect feature points in the image and describe these feature points by constructing the Hessian matrix and other steps, so as to perform subsequent processing such as image matching and target recognition.

[0036] Step S700, matching the defect feature point with the matching feature point in the template image, and selecting the best matching point based on the Euclidean distance between the matching feature point and the defect feature point, so as to obtain the defect type corresponding to the abnormal image.

[0037] Furthermore, the step of selecting the optimal matching point based on the Euclidean distance between the matching feature point and the defect feature point to obtain the defect type corresponding to the abnormal image specifically includes: Step S710, calculating the Euclidean distance between each matching feature point and the defect feature point, sorting based on the size of each Euclidean distance, filtering out some defect feature points whose Euclidean distance is greater than a preset threshold, and obtaining a target feature point set; Step S720, using the RANSAC algorithm, performs internal point determination based on the target feature point set to match the corresponding template image, thereby obtaining the defect type corresponding to the abnormal image.

[0038] The expression of the Euclidean distance corresponding to the target feature point set is as follows: ; Where d is the Euclidean distance corresponding to the target point set, and are respectively the maximum and minimum values ​​of the Euclidean distances between each of the matching feature points and the defect feature points, and k is a proportional coefficient.

[0039] Specifically, k is a proportional coefficient, which is set to 0.6-0.7 in this embodiment to remove more erroneous matching point pairs with large Euclidean distance deviations.

[0040] A preliminary screening was performed by utilizing the fact that the distance differences of the correct matching points are not large. After the preliminary screening, most of the non-related points were removed, and the intersections formed by the straight lines between the remaining related points were also greatly reduced. This not only improves the accuracy, but also reduces the matching points, shortens the detection time, and improves the detection efficiency.

[0041] In summary, the interior defect detection method in the above-mentioned embodiment of the present invention obtains the image data of the vehicle interior and obtains the detection coordinates corresponding to the welding area of ​​the interior according to the edge features of the interior, so as to select the target sub-image from the image data, and judges whether there is any abnormal welding appearance by calculating the average gray value of the target sub-image; further, the abnormal image is feature extracted by the SURF algorithm, and then the feature is matched with the template features of different defect types by the RANSAC algorithm to identify the defect type, which can effectively improve the detection efficiency compared with the global image feature extraction detection method; since the distances between the correct matching points of the weld are basically the same, some defect feature points with a Euclidean distance greater than a preset threshold are screened out, which can reduce the amount of calculation and achieve the purpose of accurate matching; by filtering the target sub-image with a low-pass filter, the influence of image illumination can be reduced; by segmenting the target sub-image with the regional growing method, the pixel points at the center point of the weld are used as the initial seed point set, and the adaptive threshold of the grayscale change is set according to the set growth conditions, the weld image can be effectively extracted, and the accuracy of subsequent defect type recognition can be improved.

[0042] Embodiment 2 The second embodiment of the present application also provides an interior defect detection system, which is used to implement the embodiments and preferred implementations, and will not be repeated here. As used below, the terms "module", "unit", "sub-unit", etc. can be a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0043] like Figure 2 As shown, the system includes: an image module 100 , an edge feature module 200 , a grayscale module 300 and a determination module 400 .

[0044] The image module 100 is used to obtain image data of vehicle interior parts, and perform perspective transformation processing on the image data to obtain a front view image; The edge feature module 200 is used to extract edge features of the front view image, obtain the center coordinates corresponding to the interior decoration part based on the edge features, and obtain the detection coordinates corresponding to the welding area based on the center coordinates and the edge features; The grayscale module 300 is used to select a target sub-image from the front view image based on the detection coordinates, calculate the average grayscale value corresponding to each pixel in the target sub-image, and determine whether the average grayscale value exceeds a preset range; The determination module 400 is used to determine that there is a welding defect in the interior decoration part if the average gray value exceeds a preset range.

[0045] Preferably, in this embodiment, the system further includes: The defect type module is used to obtain the grayscale value of the target sub-image corresponding to each welding area, select the corresponding target sub-image whose grayscale value exceeds the preset range, and mark it as an abnormal image; Extract features of the abnormal image based on the SURF algorithm to obtain defect feature points; The defect feature point is matched with the matching feature point in the template image, and the optimal matching point is selected based on the Euclidean distance between the matching feature point and the defect feature point, so as to obtain the defect type corresponding to the abnormal image.

[0046] Preferably, in this embodiment, the defect type module is specifically used for: The Euclidean distance between each matching feature point and the defect feature point is calculated, sorted based on the size of each Euclidean distance, and some defect feature points whose Euclidean distance is greater than a preset threshold are screened out to obtain a target feature point set; The RANSAC algorithm is used to perform internal point determination based on the target feature point set to match the corresponding template image, thereby obtaining the defect type corresponding to the abnormal image.

[0047] Preferably, in this embodiment, the grayscale module 300 is specifically used for: Selecting a weld center point corresponding to the weld shape from the front view image based on the detection coordinates; The pixel points on the center point of the weld are used as an initial seed point set, and a region growing method is used to segment the front view image to form a target sub-image based on the grayscale difference value between the initial seed point set and the neighboring pixel points.

[0048] In summary, the interior defect detection system in the above-mentioned embodiment of the present invention obtains the image data of the vehicle interior and obtains the detection coordinates corresponding to the welding area of ​​the interior according to the edge features of the interior, so as to select the target sub-image from the image data, and judges whether there is any abnormal welding appearance by calculating the average gray value of the target sub-image; further, the abnormal image is feature extracted by the SURF algorithm, and then the feature is matched with the template features of different defect types by the RANSAC algorithm to identify the defect type, which can effectively improve the detection efficiency compared with the global image feature extraction detection method; since the distances between the correct matching points of the weld are basically the same, some defect feature points with a Euclidean distance greater than a preset threshold are screened out, which can reduce the amount of calculation and achieve the purpose of accurate matching; by filtering the target sub-image with a low-pass filter, the influence of image illumination can be reduced; by segmenting the target sub-image with the regional growing method, the pixel points at the center point of the weld are used as the initial seed point set, and the adaptive threshold of the grayscale change is set according to the set growth conditions, the weld image can be effectively extracted, and the accuracy of subsequent defect type identification can be improved.

[0049] It should be noted that each module can be a functional module or a program module, and can be implemented by software or hardware. For modules implemented by hardware, each module can be located in the same processor; or each module can be located in different processors in any combination.

[0050] Embodiment 3 A third embodiment of the present application provides a computer, which may include a processor 81 and a memory 82 storing computer program commands.

[0051] Specifically, the processor 81 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0052] Among them, the memory 82 may include a large-capacity memory for data or commands. By way of example and not limitation, the memory 82 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 82 may include a removable or non-removable (or fixed) medium. Where appropriate, the memory 82 may be inside or outside the data processing device. In a specific embodiment, the memory 82 is a non-volatile memory. In a specific embodiment, the memory 82 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), wherein the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0053] The memory 82 may be used to store or cache various data files required for processing and / or communication, as well as possible computer program commands executed by the processor 81 .

[0054] The processor 81 implements any one of the interior decoration defect detection methods in the above-mentioned embodiments by reading and executing computer program commands stored in the memory 82 .

[0055] In some embodiments, the computer may further include a communication interface 83 and a bus 80. Figure 3 As shown, the processor 81, the memory 82, and the communication interface 83 are connected via a bus 80 and communicate with each other.

[0056] The communication interface 83 is used to implement communication between the modules, devices, units and / or equipment in the embodiment of the present application. The communication interface 83 can also implement data communication with other components such as: external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0057] The bus 80 includes hardware, software or both, and couples the components of the computer to each other. The bus 80 includes but is not limited to at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 80 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses or a combination of two or more of these. Where appropriate, bus 80 may include one or more buses. Although embodiments of the present application describe and illustrate a particular bus, the present application contemplates any suitable bus or interconnect.

[0058] Embodiment 4 The fourth embodiment of the present application provides a readable storage medium having computer program commands stored thereon; when the computer program commands are executed by a processor, any one of the interior decoration defect detection methods in the above embodiments is implemented.

[0059] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0060] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for detecting defects in interior decoration parts, characterized in that: The following steps are involved: Acquire image data of a vehicle interior trim, and perform perspective transformation processing on the image data to obtain a front view image; Extracting edge features of the front view image, acquiring center coordinates corresponding to the interior decoration part based on the edge features, and obtaining detection coordinates corresponding to the welding area based on the center coordinates and the edge features; Selecting a target sub-image from the front view image based on the detection coordinates, calculating an average grayscale value corresponding to each pixel in the target sub-image, and determining whether the average grayscale value exceeds a preset range; If the average gray value exceeds a preset range, it is determined that the interior trim has a welding defect.

2. The interior decoration defect detection method according to claim 1, characterized in that: After the step of determining that the vehicle interior trim part has a welding defect, the method further includes: Obtain the grayscale value of the target sub-image corresponding to each welding area, select the corresponding target sub-image whose grayscale value exceeds the preset range, and mark it as an abnormal image; Extract features of the abnormal image based on the SURF algorithm to obtain defect feature points; The defect feature point is matched with the matching feature point in the template image, and the optimal matching point is selected based on the Euclidean distance between the matching feature point and the defect feature point, so as to obtain the defect type corresponding to the abnormal image.

3. The interior decoration defect detection method according to claim 2, characterized in that: The step of selecting the optimal matching point based on the Euclidean distance between the matching feature point and the defect feature point to obtain the defect type corresponding to the abnormal image specifically includes: The Euclidean distance between each matching feature point and the defect feature point is calculated, sorted based on the size of each Euclidean distance, and some defect feature points whose Euclidean distance is greater than a preset threshold are screened out to obtain a target feature point set; The RANSAC algorithm is used to perform internal point determination based on the target feature point set to match the corresponding template image, thereby obtaining the defect type corresponding to the abnormal image.

4. The interior decoration defect detection method according to claim 3, characterized in that: The expression of the Euclidean distance corresponding to the target feature point set is as follows: ； Where d is the Euclidean distance corresponding to the target point set, and are respectively the maximum and minimum values ​​of the Euclidean distances between each of the matching feature points and the defect feature points, and k is a proportional coefficient.

5. The method for detecting defects in interior decoration parts according to claim 1, characterized in that: Before the step of calculating the average grayscale value corresponding to each pixel in the target sub-image, the method further includes: The target sub-image is filtered by low-pass filtering to obtain the average value of the grayscale value, so as to obtain the grayscale value of the target sub-image after processing based on the following calculation formula: ； In the formula, is the gray value of the target sub-image after processing, is the gray value of the target sub-image before processing, represents the rounding function, v is the grayscale median, is the grayscale mean, and F is the crossover frequency.

6. The interior decoration defect detection method according to claim 1, characterized in that: The step of selecting a target sub-image from the front view image based on the detection coordinates specifically includes: Selecting a weld center point corresponding to the weld shape from the front view image based on the detection coordinates; The pixel points on the center point of the weld are used as an initial seed point set, and a region growing method is used to segment the front view image to form a target sub-image based on the grayscale difference value between the initial seed point set and the neighboring pixel points.

7. The method for detecting defects in interior decoration parts according to claim 6, characterized in that: The growth condition expression corresponding to the grayscale difference value is: ； ； ； ； ； In the formula, is the weighted gray value of the pixel point in the area to be grown, is the mean gray value of the pixels in the grown area, is the adaptive threshold of grayscale change, is the preset range, n is the number of pixels in the grown area, is the gray value variance of the pixels in the grown area, is the first gray value of the weld center point, are the coordinates of the weld center point, is the second grayscale value of the neighborhood pixel, is the coordinate of the neighborhood pixel, j is the number of pixels in the field, is the third grayscale value of the surrounding pixels of the neighborhood pixel, is the coordinates of the pixels around the neighborhood pixel, k is the number of pixels around the neighborhood pixel, , , are the weight coefficients of the first grayscale value, the second grayscale value and the third grayscale value respectively.

8. An interior decoration defect detection system, characterized in that: include: An image module, used to obtain image data of vehicle interior parts, and perform perspective transformation processing on the image data to obtain a front view image; An edge feature module, used to extract edge features of the front view image, obtain the center coordinates corresponding to the interior decoration part based on the edge features, and obtain the detection coordinates corresponding to the welding area based on the center coordinates and the edge features; A grayscale module, used to select a target sub-image from the front view image based on the detection coordinates, calculate an average grayscale value corresponding to each pixel in the target sub-image, and determine whether the average grayscale value exceeds a preset range; The determination module is used to determine whether the interior decoration part has a welding defect if the average gray value exceeds a preset range.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the interior decoration part defect detection method as described in any one of claims 1-7 is implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for detecting defects in interior decoration parts as described in any one of claims 1 to 7 is implemented.

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