Product surface scratch detection method and device, processor and electronic device

Abstract

The application discloses a product surface scratch detection method and device, a processor and an electronic device. The method comprises the following steps: emitting an electromagnetic wave to the surface of a product to be detected through a millimeter wave radar, and receiving an initial signal set reflected by the surface of the product to be detected; performing three-dimensional Fourier transform on the initial signal set to obtain a plurality of phase values corresponding to the initial signal set, and determining a maximum phase value from the plurality of phase values to obtain a target phase value; calculating a phase difference value between the target phase value and a standard phase value corresponding to a standard product, and determining whether the surface of the product to be detected has a scratch according to the phase difference value. Through the application, the problem that the accuracy of product scratch detection is relatively low due to the fact that a camera needs to be used to assist in shooting a scratch image and product image omission is prone to occurring in related art is solved.

Description

Technical Field

[0001] This application relates to the field of measurement technology, and more specifically, to a method and apparatus for detecting surface scratches on a product, a processor, and electronic equipment. Background Technology

[0002] Scratches on the outer casing of an air conditioner's indoor unit affect its aesthetic appearance, impacting the customer's purchasing experience and consequently affecting sales. Furthermore, after a period of use, scratches tend to widen due to natural oxidation and environmental factors such as cleaning, potentially affecting the product's overall lifespan. In some households, the use of disinfectants like alcohol and cleaning agents can corrode the air conditioner's inner casing, making scratches more noticeable and negatively impacting the customer experience. Therefore, scratch detection on the product surface is crucial. Current technology requires camera-assisted imaging to determine scratches, but this method is susceptible to environmental factors and may result in missed images.

[0003] There is currently no effective solution to the problem that when detecting product scratches, a camera is needed to capture images of the scratches, which can easily lead to missed product images and low accuracy in scratch detection. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, processor, and electronic device for detecting surface scratches on products, in order to solve the problem in related technologies where a camera is needed to capture scratch images, which can easily lead to the omission of product images and result in low accuracy of product scratch detection.

[0005] To achieve the above objectives, according to one aspect of this application, a method for detecting surface scratches on a product is provided. The method includes: transmitting electromagnetic waves to the surface of a product to be tested using a millimeter-level radar, and receiving an initial signal set reflected back from the surface of the product to be tested; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to a target signal set, and determining the maximum phase value from the multiple phase values ​​to obtain a target phase value; calculating the phase difference between the target phase value and a standard phase value corresponding to a standard product, and determining whether a scratch exists on the surface of the product to be tested based on the phase difference, wherein the standard product is a product with a scratch-free surface.

[0006] Furthermore, before performing a three-dimensional Fourier transform on the initial signal set, the method further includes: performing cluster analysis on the initial signal set to obtain target cluster centers and target signal sets corresponding to the target cluster centers, wherein the probability value of the target signal set being a signal set of scratch reflection satisfies a preset condition; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value includes: performing a three-dimensional Fourier transform on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value.

[0007] Further, performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point includes: performing cluster analysis on the initial signals according to the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster center points and the initial signal set corresponding to the initial cluster center points; and determining the target cluster center point and the target signal set corresponding to the target cluster center point from the multiple initial cluster center points based on the number of signals in the initial signal set corresponding to the initial cluster center point.

[0008] Further, determining the target cluster center and the target signal set corresponding to the target cluster center from the plurality of initial cluster center points based on the number of signals in the initial signal set corresponding to each initial cluster center point includes: calculating the number of signals in the initial signal set corresponding to each initial cluster center point to obtain a plurality of target quantities; determining the density value corresponding to each initial signal set based on the plurality of target quantities, and using the density value as the probability value corresponding to each initial cluster center point; determining the initial cluster center point corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center point corresponding to the maximum probability value as the target cluster center point and the target signal set corresponding to the target cluster center point.

[0009] Furthermore, before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, the method further includes: filtering the signals in the initial signal set using a dynamic filter to remove noise signals from the initial signal set.

[0010] Further, determining whether there are scratches on the surface of the product to be tested based on the phase difference value includes: determining whether the phase difference value is greater than a preset phase difference value; if the phase difference value is greater than the preset phase difference value, then determining that there are scratches on the surface of the product to be tested; if the phase difference value is less than or equal to the preset phase difference value, then determining that there are no scratches on the surface of the product to be tested.

[0011] Furthermore, after determining that scratches exist on the surface of the product to be tested, the method further includes: inputting the target signal set corresponding to the target cluster center point into a deep learning algorithm; calculating the scratch size information through the deep learning algorithm to obtain the target scratch size information corresponding to the target signal set, so as to determine whether scratch repair needs to be performed based on the target scratch size information, wherein the target scratch size information includes at least: scratch depth and scratch area.

[0012] Further, determining whether scratch repair needs to be performed based on the target scratch size information includes: determining whether the scratch depth is greater than a preset depth value and determining whether the scratch area is greater than a preset area value; if the scratch depth is greater than the preset depth value, or the scratch area is greater than the preset area value, then it is determined that scratch repair needs to be performed; if the scratch depth is less than or equal to the preset depth value, and the scratch area is less than or equal to the preset area value, then it is determined that scratch repair does not need to be performed.

[0013] Furthermore, before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product, the method further includes: transmitting electromagnetic waves to the surface of the standard product through the millimeter-level radar and receiving the initial signal set reflected back from the surface of the standard product; and calculating the standard phase value corresponding to the standard product based on the initial signal set reflected back from the standard product.

[0014] To achieve the above objectives, according to another aspect of this application, a device for detecting surface scratches on a product is provided. The device includes: a first transmitting unit for transmitting electromagnetic waves to the surface of a product to be tested via a millimeter-level radar, and receiving an initial signal set reflected back from the surface of the product to be tested; a transform unit for performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain a target phase value; and a first calculation unit for calculating the phase difference between the target phase value and a standard phase value corresponding to a standard product, and determining whether a scratch exists on the surface of the product to be tested based on the phase difference, wherein the standard product is a product with a scratch-free surface.

[0015] Furthermore, the device further includes: a clustering unit, used to perform clustering analysis on the initial signal set to obtain a target cluster center point and a target signal set corresponding to the target cluster center point, wherein the probability value of the target signal set being a signal set of scratch reflection satisfies a preset condition; the transformation unit is also used to perform a three-dimensional Fourier transform on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and determine the maximum phase value from the multiple phase values ​​to obtain the target phase value.

[0016] Furthermore, the clustering unit includes: an analysis module, used to perform cluster analysis on the initial signal according to the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster centers and the initial signal set corresponding to the initial cluster centers; and a first determination module, used to determine the target cluster center and the target signal set corresponding to the target cluster center from the multiple initial cluster centers based on the number of signals in the initial signal set corresponding to the initial cluster center.

[0017] Further, the first determining module includes: a calculation submodule, used to calculate the number of signals in the initial signal set corresponding to each initial cluster center point to obtain multiple target quantities; a first determining submodule, used to determine the density value corresponding to each initial signal set based on the multiple target quantities, and use the density value as the probability value corresponding to each initial cluster center point; and a second determining submodule, used to determine the initial cluster center point corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center point corresponding to the maximum probability value as the target cluster center point and the target signal set corresponding to the target cluster center point.

[0018] Furthermore, the device further includes a processing unit, configured to filter the signals in the initial signal set using a dynamic filter before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, so as to remove noise signals in the initial signal set.

[0019] Further, the first calculation unit includes: a first judgment module, used to judge whether the phase difference value is greater than a preset phase difference value; a second determination module, used to determine that the surface of the product to be tested has scratches if the phase difference value is greater than the preset phase difference value; and a third determination module, used to determine that the surface of the product to be tested does not have scratches if the phase difference value is less than or equal to the preset phase difference value.

[0020] Furthermore, the device further includes: an input unit, used to input the target signal set corresponding to the target cluster center point into a deep learning algorithm after determining that there are scratches on the surface of the product to be inspected; and a second calculation unit, used to calculate the scratch size information through the deep learning algorithm to obtain the target scratch size information corresponding to the target signal set, so as to determine whether scratch repair needs to be performed based on the target scratch size information, wherein the target scratch size information includes at least: scratch depth and scratch area.

[0021] Further, the second calculation unit includes: a second judgment module, used to judge whether the scratch depth is greater than a preset depth value and whether the scratch area is greater than a preset area value; a fourth determination module, used to determine that scratch repair needs to be performed if the scratch depth is greater than the preset depth value or the scratch area is greater than the preset area value; and a fifth determination module, used to determine that scratch repair does not need to be performed if the scratch depth is less than or equal to the preset depth value and the scratch area is less than or equal to the preset area value.

[0022] Furthermore, the device further includes: a second transmitting unit, used to transmit electromagnetic waves to the surface of the standard product via the millimeter-level radar before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product, and to receive the initial signal set reflected back from the surface of the standard product; and a third calculating unit, used to calculate based on the initial signal set reflected back from the standard product to obtain the standard phase value corresponding to the standard product.

[0023] To achieve the above objectives, according to one aspect of this application, a processor is provided, which runs a program, wherein the program executes the product surface scratch detection method described in any one of the above claims.

[0024] To achieve the above objectives, according to one aspect of this application, an electronic device is provided, the electronic device including one or more processors and a memory, the memory being used to store the method for detecting product surface scratches implemented by the one or more processors according to any one of the above claims.

[0025] This application employs the following steps: Electromagnetic waves are emitted towards the surface of the product to be inspected using a millimeter-wave radar, and an initial signal set reflected back from the surface of the product is received; a three-dimensional Fourier transform is performed on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and the maximum phase value is determined from these multiple phase values ​​to obtain the initial phase value; the phase difference between the target phase value and the standard phase value corresponding to the standard product is calculated, and the presence of scratches on the surface of the product to be inspected is determined based on the phase difference value. The standard product is a product with no scratches. This solves the problem in related technologies where scratch detection requires camera assistance to capture scratch images, which can easily lead to missed product images and low accuracy in scratch detection. In this solution, millimeter-wave radar provides high-precision, short-range detection, unaffected by environmental factors such as light pollution and visibility during production. Furthermore, the initial signal set is subjected to a three-dimensional Fourier transform. When there are no scratches, the phase difference in the same frequency domain is relatively small after the 3D Fourier transform and fluctuates within a constant range. However, when scratches occur, the phase difference, after being transformed by 3D Fourier transform, will exceed the set critical range value. Therefore, the phase difference value can accurately determine whether there are scratches on the surface of the product to be tested, thereby improving the accuracy of scratch detection. Attached Figure Description

[0026] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0027] Figure 1 This is a flowchart of a method for detecting surface scratches on a product according to an embodiment of this application;

[0028] Figure 2 This is a flowchart of the process for determining scratches according to an embodiment of this application;

[0029] Figure 3 This is a flowchart of the process for determining scratch repair according to an embodiment of this application;

[0030] Figure 4 This is a schematic diagram of a product surface scratch detection device provided according to an embodiment of this application;

[0031] Figure 5 This is a schematic diagram of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0032] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0033] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0034] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0035] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties. For example, this system has an interface with relevant users or organizations. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain the relevant information after receiving consent information from the aforementioned user or organization.

[0036] The present invention will now be described in conjunction with preferred implementation steps. Figure 1 This is a flowchart of a product surface scratch detection method provided according to an embodiment of this application, such as... Figure 1 As shown, the method includes the following steps:

[0037] Step S101: Electromagnetic waves are emitted towards the surface of the product to be tested using a millimeter-level radar, and the initial signal set reflected back from the surface of the product to be tested is received.

[0038] Specifically, the millimeter-wave radar is fixedly installed above the production line, ensuring it can be powered on normally and that signals can be transmitted and received correctly. The millimeter-wave radar automatically detects the product casings transmitted from the production line. It emits FMCW (Frequency Modulated Wave) electromagnetic waves onto the surface of the products on the production line, and the reflected electromagnetic signals are automatically received. 3D millimeter-wave radar features strong anti-interference capabilities, small size, high precision, high resolution, and strong penetration. Furthermore, it is unaffected by environmental factors such as light pollution or low visibility during the production process. After emitting electromagnetic waves onto the surface of the product to be inspected using the millimeter-level radar, the initial signal set reflected back from the surface of the product is received.

[0039] Step S102: Perform a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determine the maximum phase value from the multiple phase values ​​to obtain the target phase value;

[0040] Specifically, a three-dimensional Fourier transform is performed on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set. The signal reflected when there are scratches on the product surface differs significantly from the signal reflected when there are no scratches. The three-dimensional Fourier transform can amplify this difference; therefore, a three-dimensional Fourier transform is performed on the initial signal set. After obtaining the multiple phase values ​​corresponding to the initial signal set, the maximum phase value is determined from these multiple phase values ​​to obtain the target phase value.

[0041] Step S103: Calculate the phase difference between the target phase value and the standard phase value corresponding to the standard product, and determine whether there are scratches on the surface of the product to be tested based on the phase difference value. The standard product is a product without scratches on its surface.

[0042] Specifically, the phase difference between the target phase value and the standard phase value corresponding to the standard product is calculated, and the presence of scratches on the surface of the product to be tested is determined by the phase difference.

[0043] In summary, millimeter-wave radar offers high-precision, short-range detection, unaffected by environmental factors such as light pollution and visibility during production. By clustering the reflected signals to obtain the target signal set corresponding to the target cluster center points, and then performing a three-dimensional Fourier transform on this target signal set, the phase difference in the same frequency domain is relatively small and fluctuates within a constant range when there are no scratches. However, when scratches are present, the phase difference exceeds the set critical range after the 3D Fourier transform. Therefore, the phase difference value can accurately determine whether there are scratches on the surface of the product being inspected, improving the accuracy of scratch detection.

[0044] It should be noted that the critical range value can be the standard angle between the current position of the product surface and the radar mounting position, that is, the angle between the current position of the product surface and the radar mounting position when there are no scratches. If the detected phase difference exceeds the standard angle value, it indicates that there is a scratch at the current position.

[0045] To improve the accuracy of scratch identification, before performing a three-dimensional Fourier transform on the initial signal set, the following steps are included: performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, wherein the probability value of the target signal set being the signal set reflected by the scratch satisfies a preset condition; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value, including: performing a three-dimensional Fourier transform on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value.

[0046] Specifically, a subtractive fuzzy C-means clustering algorithm is used to perform cluster analysis on the initial signal set to obtain the target cluster centers and the target signal sets corresponding to them. The subtractive fuzzy C-means clustering algorithm can obtain signals with distinct data attributes, which are the aforementioned target signal sets. It should be noted that the aforementioned target signal set is the signal set most likely to be scratch reflections. The subtractive fuzzy C-means clustering algorithm can calculate the density values ​​of all sample points near each cluster center, and use these density values ​​as the probability values ​​for each cluster center. The target cluster centers corresponding to the target signal sets are the cluster centers with the highest probability values, meaning the probability that the target signal set is a scratch reflection signal set satisfies the preset condition.

[0047] A three-dimensional Fourier transform is performed on the target signal set to obtain multiple phase values ​​corresponding to the target signal set. The signal reflected when there are scratches on the product surface differs significantly from the signal reflected when there are no scratches. The three-dimensional Fourier transform can amplify this difference; therefore, a three-dimensional Fourier transform is performed on the target signal set. After obtaining multiple phase values ​​corresponding to the target signal set, the maximum phase value is determined from these multiple phase values ​​to obtain the target phase value.

[0048] To improve the accuracy of product scratch detection, the product surface scratch detection method provided in this application embodiment includes performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point. This includes: performing cluster analysis on the initial signals according to the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster center points and the initial signal set corresponding to the initial cluster center points; and determining the target cluster center point and the target signal set corresponding to the target cluster center point from the multiple initial cluster center points based on the number of signals in the initial signal set corresponding to the initial cluster center point.

[0049] Specifically, the initial signal is clustered using a subtractive fuzzy C-means clustering algorithm to divide it into multiple categories, resulting in multiple initial cluster centers and their corresponding initial signal sets. Then, the number of signals in each initial signal set corresponding to a given cluster center is calculated. Based on this signal count, the target cluster center and its corresponding target signal set are determined from the multiple initial cluster centers. The subtractive fuzzy C-means clustering algorithm can accurately and quickly identify which information sets are most likely to be scratch reflection signals.

[0050] It is crucial to determine the target cluster center and the target signal set corresponding to the target cluster center from multiple initial cluster centers based on the number of signals in the initial signal set corresponding to the initial cluster center. Therefore, the product surface scratch detection method provided in this application embodiment further includes the following steps: calculating the number of signals in the initial signal set corresponding to each initial cluster center to obtain multiple target quantities; determining the density value corresponding to each initial signal set based on the multiple target quantities, and using the density value as the probability value corresponding to each initial cluster center; determining the initial cluster center corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center corresponding to the maximum probability value as the target cluster center and the target signal set corresponding to the target cluster center.

[0051] Specifically, the number of signals in the initial signal set corresponding to each initial cluster center is calculated, and the density value corresponding to each initial signal set is calculated based on the number of signals. This density value is then used as the probability value corresponding to each initial cluster center. Finally, the initial cluster center corresponding to the highest probability value and the initial signal set corresponding to the initial cluster center corresponding to the highest probability value are used as the target cluster center and the target signal set corresponding to the target cluster center. Since the signals reflected from scratches differ significantly from those reflected from areas without scratches, and the subtractive fuzzy C-means clustering algorithm can obtain pure signals with clear data attributes, it can accurately and quickly identify which information sets are most likely to be scratch-reflected signal sets.

[0052] To improve the accuracy of cluster analysis, in the product surface scratch detection method provided in this application embodiment, before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, the method further includes: filtering the signals in the initial signal set through a dynamic filter to remove noise signals in the initial signal set.

[0053] Specifically, after obtaining the initial signal set reflected from the product surface, a dynamic filter is used to filter the reflected initial signal set, discarding points with low signal-to-noise ratios to remove environmental clutter. This filtering operation effectively removes environmental clutter and improves the accuracy of subsequent cluster analysis.

[0054] Since the phase difference in the same frequency domain is relatively small after 3D Fourier transform when there are no scratches, and it fluctuates within a constant range, but when scratches appear, the phase difference will exceed the set critical range value after 3D Fourier transform. Therefore, in the product surface scratch detection method provided in this application embodiment, determining whether there are scratches on the surface of the product to be tested based on the phase difference value includes: judging whether the phase difference value is greater than a preset phase difference value; if the phase difference value is greater than the preset phase difference value, it is determined that there are scratches on the surface of the product to be tested; if the phase difference value is less than or equal to the preset phase difference value, it is determined that there are no scratches on the surface of the product to be tested.

[0055] Specifically, after calculating the phase difference value, it is determined whether the phase difference value is greater than the preset phase difference value. If the phase difference value is greater than the preset phase difference value, then there are scratches on the surface of the product to be tested; if the phase difference value is less than or equal to the preset phase difference value, then there are no scratches on the surface of the product to be tested.

[0056] In an alternative embodiment, the following can be employed: Figure 2 The flowchart shown illustrates the scratch detection process. Specifically, a millimeter-wave radar is fixedly installed above the production line, ensuring it is powered on and capable of transmitting and receiving signals normally. The millimeter-wave radar emits FMCW electromagnetic waves towards the products on the production line, automatically reflecting the electromagnetic wave signals back. The phase difference between the reflected signal and the original scratch-free signal is calculated, and the presence of scratches on the product surface is determined based on this phase difference.

[0057] It should be noted that the preset phase difference value can be the standard angle between the current position of the product surface and the radar mounting position, that is, the angle between the current position of the product surface and the radar mounting position when there are no scratches. If the detected phase difference exceeds the standard angle value, it indicates that there are scratches at the current position.

[0058] After determining that scratches exist on the product surface, it is necessary to determine whether the scratches need to be repaired based on information such as the size of the scratches. Therefore, in the product surface scratch detection method provided in this application embodiment, after determining that scratches exist on the surface of the product to be detected, the method further includes: inputting the target signal set corresponding to the target cluster center point into a deep learning algorithm; calculating the scratch size information through the deep learning algorithm to obtain the target scratch size information corresponding to the target signal set, so as to determine whether scratch repair needs to be performed based on the target scratch size information, wherein the target scratch size information includes at least: scratch depth and scratch area.

[0059] Determining whether scratch repair needs to be performed based on the target scratch size information includes: determining whether the scratch depth is greater than a preset depth value and whether the scratch area is greater than a preset area value; if the scratch depth is greater than the preset depth value or the scratch area is greater than the preset area value, then scratch repair needs to be performed; if the scratch depth is less than or equal to the preset depth value and the scratch area is less than or equal to the preset area value, then scratch repair does not need to be performed.

[0060] Specifically, the target signal set corresponding to the target cluster center point is input into the deep learning algorithm. The deep learning algorithm calculates based on the target signal set to obtain information such as the scratch depth, size, and area, which is the target scratch size information mentioned above. After comparing the target scratch size information with the set standard values ​​(i.e., the preset depth value and preset area value mentioned above), it is determined whether the scratch needs to be repaired. It should be noted that the set standard values ​​can include: data values ​​without scratches and standard deviation values ​​with scratches. The standard values ​​with scratches can be set according to national standards.

[0061] The system judges based on preset standard values. If the scratch depth is greater than the preset depth value or the scratch area is greater than the preset area value, scratch repair needs to be performed. If the scratch depth is less than or equal to the preset depth value and the scratch area is less than or equal to the preset area value, scratch repair does not need to be performed.

[0062] After identifying the scratches, we further confirm the three-dimensional data information such as the size, depth, and area of ​​the scratches, and determine whether to repair them based on this data, thus ensuring the quality of the manufactured products.

[0063] Before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product, the standard phase value is calculated through the following steps: electromagnetic waves are emitted to the surface of the standard product through a millimeter-level radar, and the initial signal set reflected back from the surface of the standard product is received; the standard phase value corresponding to the standard product is calculated based on the initial signal set reflected back from the standard product.

[0064] In an alternative embodiment, the following can be employed: Figure 3 The flowchart shown illustrates a method for detecting surface scratches on products. A millimeter-wave radar is fixedly installed above the production line, ensuring it is powered on and capable of transmitting and receiving signals normally. The millimeter-wave radar emits FMCW electromagnetic waves towards the products on the production line, automatically reflecting the electromagnetic wave signals back. The phase difference between the reflected signal and the original scratch-free signal is calculated, and the presence of scratches on the product surface is determined based on this phase difference. After confirming the presence of scratches, the length, depth, and area of ​​the scratches are determined using the reflected signal. Based on this data, a decision is made regarding whether scratch repair is necessary.

[0065] This solution employs millimeter-wave radar for non-contact scratch detection. After obtaining 3D data, deep learning algorithms are used to further confirm the size, depth, area, and other three-dimensional data of the scratches. The 3D data is then used to determine whether repairs are needed on the air conditioner's indoor unit casing and whether the product should be discarded, thus ensuring product quality. Furthermore, this method avoids the high requirements for a clean environment during image acquisition, improving the accuracy of scratch detection on product surfaces.

[0066] The product surface scratch detection method provided in this application embodiment emits electromagnetic waves to the surface of the product to be tested using a millimeter-wave radar and receives the initial signal set reflected back from the surface of the product to be tested. A three-dimensional Fourier transform is performed on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set. The maximum phase value is determined from these multiple phase values ​​to obtain the initial phase value. The phase difference between the target phase value and the standard phase value corresponding to the standard product is calculated, and the presence of scratches on the surface of the product to be tested is determined based on the phase difference value. The standard product is a product with no scratches. This solves the problem in related technologies where scratch detection requires a camera to assist in capturing scratch images, which can easily lead to missed product images and low accuracy in scratch detection. In this solution, millimeter-wave radar provides high-precision, short-range detection, unaffected by environmental factors such as light pollution and visibility during production. Then, a three-dimensional Fourier transform is performed on the initial signal set. When there are no scratches, the phase difference in the same frequency domain is relatively small after the 3D Fourier transform and fluctuates within a constant range. However, when scratches occur, the phase difference, after being transformed by 3D Fourier transform, will exceed the set critical range value. Therefore, the phase difference value can accurately determine whether there are scratches on the surface of the product to be tested, thereby improving the accuracy of scratch detection.

[0067] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0068] This application also provides a device for detecting surface scratches on a product. It should be noted that this device can be used to execute the method for detecting surface scratches on a product provided in this application. The following describes the device for detecting surface scratches on a product provided in this application.

[0069] Figure 4 This is a schematic diagram of a product surface scratch detection device according to an embodiment of this application. Figure 4 As shown, the device includes: a first transmitting unit 401, a conversion unit 402, and a first computing unit 403.

[0070] The first transmitting unit 401 is used to transmit electromagnetic waves to the surface of the product to be tested via millimeter-level radar and to receive the initial signal set reflected back from the surface of the product to be tested.

[0071] The transformation unit 402 is used to perform a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and to determine the maximum phase value from the multiple phase values ​​to obtain the target phase value;

[0072] The first calculation unit 403 is used to calculate the phase difference between the target phase value and the standard phase value corresponding to the standard product, and to determine whether there are scratches on the surface of the product to be tested based on the phase difference value, wherein the standard product is a product without scratches on the surface.

[0073] The product surface scratch detection device provided in this application embodiment transmits electromagnetic waves to the surface of the product to be detected via a millimeter-wave radar through a first transmitting unit 401, and receives the initial signal set reflected back from the surface of the product to be detected. A transformation unit 402 performs a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determines the maximum phase value from the multiple phase values ​​to obtain the target phase value. A first calculation unit 403 calculates the phase difference between the target phase value and the standard phase value corresponding to the standard product, and determines whether there are scratches on the surface of the product to be detected based on the phase difference value. The standard product is a product without scratches. This solves the problem in related technologies that when detecting product scratches, a camera is needed to help capture scratch images, which can easily lead to missed product images and low accuracy in product scratch detection. In this solution, based on the high-precision close-range detection of millimeter-wave radar, it is not affected by the visibility of the production environment, such as light pollution. Then, a three-dimensional Fourier transform is performed on the initial signal set. When there are no scratches, the phase difference in the same frequency domain is relatively small after the 3D Fourier transform and will fluctuate within a constant range. However, when scratches occur, the phase difference, after being transformed by 3D Fourier transform, will exceed the set critical range value. Therefore, the phase difference value can accurately determine whether there are scratches on the surface of the product to be tested, thereby improving the accuracy of scratch detection.

[0074] Optionally, in the product surface scratch detection device provided in the embodiments of this application, the device further includes: a clustering unit, used to perform cluster analysis on the initial signal set to obtain a target cluster center point and a target signal set corresponding to the target cluster center point, wherein the probability value of the target signal set being a signal set of scratch reflection satisfies a preset condition; the transformation unit is further used to perform a three-dimensional Fourier transform on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and to determine the maximum phase value from the multiple phase values ​​to obtain the target phase value.

[0075] Optionally, in the product surface scratch detection device provided in this application embodiment, the clustering unit includes: an analysis module, used to perform cluster analysis on the initial signal according to the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster centers and the initial signal set corresponding to the initial cluster centers; and a first determination module, used to determine the target cluster center and the target signal set corresponding to the target cluster center from the multiple initial cluster centers based on the number of signals in the initial signal set corresponding to the initial cluster center.

[0076] Optionally, in the product surface scratch detection device provided in this application embodiment, the first determining module includes: a calculation submodule, used to calculate the number of signals in the initial signal set corresponding to each initial cluster center point to obtain multiple target quantities; a first determining submodule, used to determine the density value corresponding to each initial signal set based on the multiple target quantities, and use the density value as the probability value corresponding to each initial cluster center point; and a second determining submodule, used to determine the initial cluster center point corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center point corresponding to the maximum probability value as the target cluster center point and the target signal set corresponding to the target cluster center point.

[0077] Optionally, in the product surface scratch detection device provided in the embodiments of this application, the device further includes: a processing unit, used to filter the signals in the initial signal set by a dynamic filter before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, so as to remove the noise signals in the initial signal set.

[0078] Optionally, in the product surface scratch detection device provided in this application embodiment, the first calculation unit includes: a first judgment module, used to judge whether the phase difference value is greater than a preset phase difference value; a second determination module, used to determine that there is a scratch on the surface of the product to be tested if the phase difference value is greater than the preset phase difference value; and a third determination module, used to determine that there is no scratch on the surface of the product to be tested if the phase difference value is less than or equal to the preset phase difference value.

[0079] Optionally, in the product surface scratch detection device provided in this application embodiment, the device further includes: an input unit, used to input the target signal set corresponding to the target cluster center point into a deep learning algorithm after determining that there are scratches on the surface of the product to be detected; and a second calculation unit, used to calculate the scratch size information through the deep learning algorithm to obtain the target scratch size information corresponding to the target signal set, so as to determine whether scratch repair needs to be performed based on the target scratch size information, wherein the target scratch size information includes at least: scratch depth and scratch area.

[0080] Optionally, in the product surface scratch detection device provided in this application embodiment, the second calculation unit includes: a second judgment module, used to judge whether the scratch depth is greater than a preset depth value and whether the scratch area is greater than a preset area value; a fourth determination module, used to determine that scratch repair needs to be performed if the scratch depth is greater than the preset depth value or the scratch area is greater than the preset area value; and a fifth determination module, used to determine that scratch repair does not need to be performed if the scratch depth is less than or equal to the preset depth value and the scratch area is less than or equal to the preset area value.

[0081] Optionally, in the product surface scratch detection device provided in the embodiments of this application, the device further includes: a second transmitting unit, used to transmit electromagnetic waves to the surface of the standard product through a millimeter-level radar and receive the initial signal set reflected back from the surface of the standard product before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product; and a third calculating unit, used to calculate based on the initial signal set reflected back from the standard product to obtain the standard phase value corresponding to the standard product.

[0082] The device for detecting surface scratches on a product includes a processor and a memory. The first transmitting unit 401, the conversion unit 402, and the first calculation unit 403 mentioned above are all stored in the memory as program units. The processor executes the program units stored in the memory to achieve the corresponding functions.

[0083] The processor contains a kernel, which retrieves the corresponding program unit from memory. One or more kernels can be configured, and surface scratch detection on the product can be achieved by adjusting the kernel parameters.

[0084] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0085] This invention provides a processor for running a program, wherein the program executes a method for detecting scratches on the surface of a product.

[0086] like Figure 5 As shown, this embodiment of the invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: transmitting electromagnetic waves to the surface of a product to be tested via a millimeter-level radar and receiving an initial signal set reflected back from the surface of the product to be tested; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain a target phase value; calculating the phase difference between the target phase value and the standard phase value corresponding to a standard product, and determining whether there are scratches on the surface of the product to be tested based on the phase difference, wherein the standard product is a product without scratches on its surface.

[0087] Optionally, before performing a three-dimensional Fourier transform on the initial signal set, the method further includes: performing cluster analysis on the initial signal set to obtain target cluster center points and target signal sets corresponding to the target cluster center points, wherein the probability value of the target signal set being a signal set of scratch reflection satisfies a preset condition; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value, including: performing a three-dimensional Fourier transform on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value.

[0088] Optionally, performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point includes: performing cluster analysis on the initial signals according to the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster center points and the initial signal sets corresponding to the initial cluster center points; and determining the target cluster center point and the target signal set corresponding to the target cluster center point from the multiple initial cluster center points based on the number of signals in the initial signal sets corresponding to the initial cluster center points.

[0089] Optionally, determining the target cluster center and the target signal set corresponding to the target cluster center from multiple initial cluster centers based on the number of signals in the initial signal set corresponding to the initial cluster center includes: calculating the number of signals in the initial signal set corresponding to each initial cluster center to obtain multiple target quantities; determining the density value corresponding to each initial signal set based on the multiple target quantities, and using the density value as the probability value corresponding to each initial cluster center; determining the initial cluster center corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center corresponding to the maximum probability value as the target cluster center and the target signal set corresponding to the target cluster center.

[0090] Optionally, before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, the method further includes: filtering the signals in the initial signal set using a dynamic filter to remove noise signals from the initial signal set.

[0091] Optionally, determining whether there are scratches on the surface of the product to be tested based on the phase difference value includes: determining whether the phase difference value is greater than a preset phase difference value; if the phase difference value is greater than the preset phase difference value, then determining that there are scratches on the surface of the product to be tested; if the phase difference value is less than or equal to the preset phase difference value, then determining that there are no scratches on the surface of the product to be tested.

[0092] Optionally, after determining that there are scratches on the surface of the product to be tested, the method further includes: inputting the target signal set corresponding to the target cluster center point into a deep learning algorithm; calculating the scratch size information through the deep learning algorithm to obtain the target scratch size information corresponding to the target signal set, so as to determine whether scratch repair needs to be performed based on the target scratch size information, wherein the target scratch size information includes at least: scratch depth and scratch area.

[0093] Optionally, determining whether scratch repair needs to be performed based on the target scratch size information includes: determining whether the scratch depth is greater than a preset depth value and whether the scratch area is greater than a preset area value; if the scratch depth is greater than the preset depth value, or the scratch area is greater than the preset area value, then it is determined that scratch repair needs to be performed; if the scratch depth is less than or equal to the preset depth value and the scratch area is less than or equal to the preset area value, then it is determined that scratch repair does not need to be performed.

[0094] Optionally, before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product, the method further includes: transmitting electromagnetic waves to the surface of the standard product via a millimeter-level radar and receiving the initial signal set reflected back from the surface of the standard product; and calculating the standard phase value corresponding to the standard product based on the initial signal set reflected back from the standard product.

[0095] The devices mentioned in this article can be servers, PCs, tablets, mobile phones, etc.

[0096] This application also provides a computer program product, which, when executed on a data processing device, is suitable for executing an initialization program with the following steps: transmitting electromagnetic waves to the surface of a product to be tested via a millimeter-level radar and receiving an initial signal set reflected back from the surface of the product to be tested; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain a target phase value; calculating the phase difference between the target phase value and the standard phase value corresponding to a standard product, and determining whether there are scratches on the surface of the product to be tested based on the phase difference, wherein the standard product is a product with no scratches on its surface.

[0097] Optionally, before performing a three-dimensional Fourier transform on the initial signal set, the method further includes: performing cluster analysis on the initial signal set to obtain target cluster center points and target signal sets corresponding to the target cluster center points, wherein the probability value of the target signal set being a signal set of scratch reflection satisfies a preset condition; performing a three-dimensional Fourier transform on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value, including: performing a three-dimensional Fourier transform on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and determining the maximum phase value from the multiple phase values ​​to obtain the target phase value.

[0098] Optionally, performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point includes: performing cluster analysis on the initial signals according to the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster center points and the initial signal sets corresponding to the initial cluster center points; and determining the target cluster center point and the target signal set corresponding to the target cluster center point from the multiple initial cluster center points based on the number of signals in the initial signal sets corresponding to the initial cluster center points.

[0099] Optionally, determining the target cluster center and the target signal set corresponding to the target cluster center from multiple initial cluster centers based on the number of signals in the initial signal set corresponding to the initial cluster center includes: calculating the number of signals in the initial signal set corresponding to each initial cluster center to obtain multiple target quantities; determining the density value corresponding to each initial signal set based on the multiple target quantities, and using the density value as the probability value corresponding to each initial cluster center; determining the initial cluster center corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center corresponding to the maximum probability value as the target cluster center and the target signal set corresponding to the target cluster center.

[0100] Optionally, before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, the method further includes: filtering the signals in the initial signal set using a dynamic filter to remove noise signals from the initial signal set.

[0101] Optionally, determining whether there are scratches on the surface of the product to be tested based on the phase difference value includes: determining whether the phase difference value is greater than a preset phase difference value; if the phase difference value is greater than the preset phase difference value, then determining that there are scratches on the surface of the product to be tested; if the phase difference value is less than or equal to the preset phase difference value, then determining that there are no scratches on the surface of the product to be tested.

[0102] Optionally, after determining that there are scratches on the surface of the product to be tested, the method further includes: inputting the target signal set corresponding to the target cluster center point into a deep learning algorithm; calculating the scratch size information through the deep learning algorithm to obtain the target scratch size information corresponding to the target signal set, so as to determine whether scratch repair needs to be performed based on the target scratch size information, wherein the target scratch size information includes at least: scratch depth and scratch area.

[0103] Optionally, determining whether scratch repair needs to be performed based on the target scratch size information includes: determining whether the scratch depth is greater than a preset depth value and whether the scratch area is greater than a preset area value; if the scratch depth is greater than the preset depth value, or the scratch area is greater than the preset area value, then it is determined that scratch repair needs to be performed; if the scratch depth is less than or equal to the preset depth value and the scratch area is less than or equal to the preset area value, then it is determined that scratch repair does not need to be performed.

[0104] Optionally, before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product, the method further includes: transmitting electromagnetic waves to the surface of the standard product via a millimeter-level radar and receiving the initial signal set reflected back from the surface of the standard product; and calculating the standard phase value corresponding to the standard product based on the initial signal set reflected back from the standard product.

[0105] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0107] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0108] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0109] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0110] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0111] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0112] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0113] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for detecting surface scratches on a product, characterized in that, include: Electromagnetic waves are emitted onto the surface of the product to be tested using millimeter-level radar, and the initial set of signals reflected back from the surface of the product to be tested is received. A three-dimensional Fourier transform is performed on the initial signal set to obtain multiple phase values ​​corresponding to the initial signal set, and the maximum phase value is determined from the multiple phase values ​​to obtain the target phase value; Calculate the phase difference between the target phase value and the standard phase value corresponding to the standard product, and determine whether there are scratches on the surface of the product to be tested based on the phase difference, wherein the standard product is a product without scratches on its surface; Before performing a three-dimensional Fourier transform on the initial signal set, the method further includes: Cluster analysis is performed on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point. The cluster analysis adopts the subtractive fuzzy C-means clustering algorithm. The target signal set is the signal set corresponding to the cluster center point whose probability value satisfies the preset condition of the signal set of scratch reflection. The probability value is calculated based on the following steps: calculate the density value of all sample points in the neighborhood of the cluster center point, and use the density value as the probability value of the signal set corresponding to the cluster center point.

2. The detection method according to claim 1, characterized in that, Before performing a three-dimensional Fourier transform on the initial signal set, the method further includes: Performing a three-dimensional Fourier transform on the initial signal set yields multiple phase values ​​corresponding to the initial signal set. The maximum phase value is then determined from these multiple phase values ​​to obtain the target phase value, which includes: A three-dimensional Fourier transform is performed on the target signal set to obtain multiple phase values ​​corresponding to the target signal set, and the maximum phase value is determined from the multiple phase values ​​to obtain the target phase value.

3. The detection method according to claim 1, characterized in that, Cluster analysis is performed on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, including: The initial signal is clustered using the subtractive fuzzy C-means clustering algorithm to obtain multiple initial cluster centers and the initial signal set corresponding to the initial cluster centers; Based on the number of signals in the initial signal set corresponding to the initial cluster center point, the target cluster center point and the target signal set corresponding to the target cluster center point are determined from the plurality of initial cluster center points.

4. The detection method according to claim 3, characterized in that, Determining the target cluster center and the target signal set corresponding to the target cluster center from the plurality of initial cluster center points, based on the number of signals in the initial signal set corresponding to the initial cluster center point, includes: Calculate the number of signals in the initial signal set corresponding to each initial cluster center point to obtain the number of multiple targets; Based on the number of multiple targets, a density value corresponding to each initial signal set is determined, and the density value is used as the probability value corresponding to each initial cluster center point. The initial cluster center point corresponding to the maximum probability value and the initial signal set corresponding to the initial cluster center point corresponding to the maximum probability value are determined as the target cluster center point and the target signal set corresponding to the target cluster center point.

5. The detection method according to claim 2, characterized in that, Before performing cluster analysis on the initial signal set to obtain the target cluster center point and the target signal set corresponding to the target cluster center point, the method further includes: The signals in the initial signal set are filtered by a dynamic filter to remove noise signals from the initial signal set.

6. The detection method according to claim 2, characterized in that, Determining whether there are scratches on the surface of the product to be tested based on the phase difference value includes: Determine whether the phase difference value is greater than a preset phase difference value; If the phase difference value is greater than the preset phase difference value, it is determined that there are scratches on the surface of the product to be tested; If the phase difference value is less than or equal to the preset phase difference value, it is determined that there are no scratches on the surface of the product to be tested.

7. The detection method according to claim 6, characterized in that, After determining that scratches exist on the surface of the product to be tested, the method further includes: The target signal set corresponding to the target cluster center point is input into the deep learning algorithm; The deep learning algorithm is used to calculate the scratch size information to obtain the target scratch size information corresponding to the target signal set. The target scratch size information is used to determine whether scratch repair needs to be performed. The target scratch size information includes at least scratch depth and scratch area.

8. The detection method according to claim 7, characterized in that, Determining whether scratch repair needs to be performed based on the target scratch size information includes: Determine whether the scratch depth is greater than a preset depth value, and determine whether the scratch area is greater than a preset area value; If the scratch depth is greater than the preset depth value, or the scratch area is greater than the preset area value, then it is determined that scratch repair needs to be performed; If the scratch depth is less than or equal to the preset depth value, and the scratch area is less than or equal to the preset area value, then it is determined that scratch repair does not need to be performed.

9. The detection method according to claim 1, characterized in that, Before calculating the phase difference between the target phase value and the standard phase value corresponding to the standard product, the method further includes: The millimeter-class radar emits electromagnetic waves toward the surface of the standard product and receives the initial set of signals reflected back from the surface of the standard product. The standard phase value corresponding to the standard product is obtained by calculating based on the initial signal set reflected back from the standard product.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the product surface scratch detection method according to any one of claims 1 to 9.

11. An electronic device, characterized in that, The device includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the product surface scratch detection method according to any one of claims 1 to 9.

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