Radar Lens Stain Detection and Device Applicable to Mine Environment

Abstract

This application proposes a radar lens stain detection and device applicable to the mine environment. Among them, the method includes: obtaining the prior information of the radar scanning window, where the prior information includes the curvature and roughness of each point of the radar scanning window in the clean state; obtaining the three-dimensional point cloud data and infrared image of the radar scanning window collected by the structured light camera and the infrared camera respectively; converting the infrared image into a binary image, and connecting the black areas of the binary image to obtain the area to be detected; based on the three-dimensional point cloud data of the radar scanning window, obtaining the three-dimensional point cloud data of the area to be detected; and calculating the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected; based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and the prior information, determining whether the radar lens is contaminated by stains; solving the problem that the radar lens stain detection in the related art is not universal.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular, to a radar lens stain detection and device applicable to mine environments. Background Art

[0002] In a mine environment, point clouds of the scene can be collected by a radar, and operations such as target recognition can be performed through the point clouds. Due to the poor mine environment, the radar lens may be covered by pollutants such as dust and water mist, thereby affecting the effectiveness of the data collected by the radar.

[0003] In the prior art, radar lens stain detection is performed by controlling different laser beams to be excited successively, and then processing and discriminating according to the differences in the voltage signals obtained by the receiving device. The disadvantage of this method is that it is only applicable to non-coaxial lidar and does not have universality. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems in the related art to some extent.

[0005] To this end, the first object of this application is to propose a radar lens stain detection method applicable to mine environments to achieve universal radar lens stain detection and solve the problem that radar lens stain detection in the related art does not have universality.

[0006] The second object of this application is to propose a radar lens stain detection device applicable to mine environments.

[0007] The third object of this application is to propose another radar lens stain detection device applicable to mine environments.

[0008] The fourth object of this application is to propose a computer-readable storage medium.

[0009] The fifth object of this application is to propose a computer program product.

[0010] To achieve the above object, the first aspect embodiment of this application proposes a radar lens stain detection method applicable to mine environments, including:

[0011] Obtaining prior information of the radar scanning window, where the prior information includes the curvature and roughness of each point of the radar scanning window in a clean state;

[0012] Obtaining three-dimensional point cloud data and infrared images of the radar scanning window collected by a structured light camera and an infrared camera respectively;

[0013] Converting the infrared image into a binary image and connecting the black regions of the binary image to obtain a region to be detected;

[0014] Based on the three-dimensional point cloud data of the radar scanning window, obtain the three-dimensional point cloud data of the area to be detected; and calculate the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected.

[0015] Based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and the prior information, determine whether the radar lens is contaminated by stains.

[0016] In some implementation manners, obtaining the prior information of the radar scanning window includes:

[0017] Collect data of the radar scanning window in a clean state through the structured light camera to obtain the three-dimensional point cloud data of the clean radar scanning window.

[0018] Based on the three-dimensional point cloud data of the clean radar scanning window, calculate the curvature and roughness of each point of the radar scanning window in the clean state.

[0019] In some implementation manners, calculating the curvature and roughness of each point of the radar scanning window in the clean state based on the three-dimensional point cloud data of the clean radar scanning window includes:

[0020] Based on the three-dimensional point cloud data of the clean radar scanning window, obtain the curvature of each point by using the principal component analysis method for the neighborhood of each point.

[0021] Based on the three-dimensional point cloud data of the clean radar scanning window, obtain the fitting error by performing least squares fitting on the neighborhood of each point, and use the fitting error as the roughness of the corresponding point to obtain the roughness of each point.

[0022] In some implementation manners, after calculating the curvature and roughness of each point of the radar scanning window in the clean state based on the three-dimensional point cloud data of the clean radar scanning window, it includes:

[0023] Store each point of the radar scanning window in the clean state and the curvature and roughness of each point in a multi-dimensional search tree.

[0024] In some implementation manners, converting the infrared image into a binary image and connecting the black regions of the binary image to obtain the area to be detected includes:

[0025] Convert the infrared image into a temperature map.

[0026] Map the temperature map to a grayscale image.

[0027] Perform adaptive binarization on the grayscale image to obtain a binarized image.

[0028] Connect the black regions in the binary image through a region growing algorithm to obtain the region to be detected.

[0029] In some implementation manners, determining whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected and the prior information includes:

[0030] Determine the prior three-dimensional points closest to each point in the region to be detected from the prior information, and obtain the curvature and roughness of each prior three-dimensional point;

[0031] Based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected, calculate the average curvature and average roughness of the region to be detected;

[0032] Based on the curvature and roughness of each prior three-dimensional point, calculate the average curvature and average roughness of the prior region;

[0033] Calculate a first difference between the average curvature of the region to be detected and the average curvature of the prior region, and a second difference between the average roughness of the region to be detected and the average roughness of the prior region;

[0034] Based on the first difference and the second difference, determine whether the radar lens is contaminated by stains.

[0035] In some implementation manners, determining whether the radar lens is contaminated by stains based on the first difference and the second difference includes:

[0036] Judge whether the first difference is greater than the curvature change threshold and whether the second difference is greater than the roughness change threshold;

[0037] In the case where the first difference is greater than the curvature change threshold and the second difference is greater than the roughness change threshold, determine that the radar lens is contaminated by stains.

[0038] To achieve the above object, a radar lens stain detection device applicable to a mine environment according to a second aspect embodiment of the present application includes:

[0039] A data processing module, configured to obtain prior information of a radar scanning window, where the prior information includes the curvature and roughness of each point of the radar scanning window in a clean state;

[0040] A data acquisition module, configured to acquire three-dimensional point cloud data and infrared images of the radar scanning window respectively collected by a structured light camera and an infrared camera;

[0041] An image processing module, configured to convert the infrared image into a binary image and connect the black regions of the binary image to obtain a region to be detected;

[0042] The data processing module is further configured to obtain the three-dimensional point cloud data of the region to be detected based on the three-dimensional point cloud data of the radar scanning window; and calculate the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected;

[0043] The data processing module is further configured to determine whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected and the prior information.

[0044] In some implementation manners, when obtaining the prior information of the radar scanning window, the data processing module is configured to:

[0045] Collect data of the radar scanning window in a clean state through the structured light camera to obtain the three-dimensional point cloud data of the clean radar scanning window;

[0046] Based on the three-dimensional point cloud data of the clean radar scanning window, calculate the curvature and roughness of each point of the radar scanning window in the clean state.

[0047] In some implementation manners, when calculating the curvature and roughness of each point of the radar scanning window in the clean state based on the three-dimensional point cloud data of the clean radar scanning window, the data processing module is configured to:

[0048] Based on the three-dimensional point cloud data of the clean radar scanning window, obtain the curvature of each point by using the principal component analysis method for the neighborhood of each point;

[0049] Based on the three-dimensional point cloud data of the clean radar scanning window, obtain the fitting error by performing least squares fitting on the neighborhood of each point, and use the fitting error as the roughness of the corresponding point to obtain the roughness of each point.

[0050] In some implementation manners, the data processing module is further configured to:

[0051] Store each point of the radar scanning window in the clean state and the curvature and roughness of each point in a multi-dimensional search tree.

[0052] In some implementation manners, the image processing module is specifically configured to:

[0053] Convert the infrared image into a temperature map;

[0054] Map the temperature map to a grayscale map;

[0055] The grayscale image is processed by adaptive binarization to obtain a binary image;

[0056] The black regions in the binary image are connected by a region growing algorithm to obtain a region to be detected.

[0057] In some implementation manners, when the data processing module determines whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected and the prior information, it is used for:

[0058] Determine the prior three-dimensional points closest to each point in the region to be detected from the prior information, and obtain the curvature and roughness of each prior three-dimensional point;

[0059] Based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected, calculate the average curvature and average roughness of the region to be detected;

[0060] Based on the curvature and roughness of each prior three-dimensional point, calculate the average curvature and average roughness of the prior region;

[0061] Calculate a first difference between the average curvature of the region to be detected and the average curvature of the prior region, and a second difference between the average roughness of the region to be detected and the average roughness of the prior region;

[0062] Based on the first difference and the second difference, determine whether the radar lens is contaminated by stains.

[0063] In some implementation manners, when the data processing module determines whether the radar lens is contaminated by stains based on the first difference and the second difference, it is used for:

[0064] Judge whether the first difference is greater than the curvature change threshold and whether the second difference is greater than the roughness change threshold;

[0065] In the case where the first difference is greater than the curvature change threshold and the second difference is greater than the roughness change threshold, determine that the radar lens is contaminated by stains.

[0066] To achieve the above object, an embodiment of the third aspect of the present application provides a radar lens stain detection device applicable to a mine environment, including a detection camera, a rotatable bracket, and a processor. The detection camera includes an infrared camera and a structured light camera fixedly connected. A lens protection cover is provided outside the lens of the detection camera. The detection camera is communicatively connected to the processor. One end of the rotatable bracket is fixedly connected to the detection camera. During detection, the lens of the detection camera faces the radar to be detected. After detection, the rotatable bracket rotates to move the detection camera outside the field of view angle of the radar to be detected.

[0067] The structured light camera and the infrared camera are respectively used to obtain the three-dimensional point cloud data and the infrared image of the radar scanning window of the radar to be detected.

[0068] The processor is configured to implement the method described in the first aspect based on the three-dimensional point cloud data and the infrared image collected by the detection camera.

[0069] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, are used to implement the method described in the first aspect.

[0070] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0071] The radar lens stain detection method, device, electronic device, and storage medium applicable to the mine environment provided by the present application obtain the curvature and roughness of the point cloud data of the radar lens area in the clean state and the curvature and roughness of the point cloud data of the radar lens area during the current detection in advance through a structured camera and an infrared camera, and compare the point cloud data features in the clean state with the point cloud data features during the current detection. According to the data change situation, it is determined that the radar lens is contaminated by stains. The radar lens stain detection method of the present invention can be used for the stain detection of radar lenses in a mine environment, is applicable to any type of radar device, and has universality. Once a stain is detected, the stain can be removed by manual or automatic cleaning devices in a timely manner.

[0072] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0074] Figure 1 Schematic flowchart of a method for detecting stains on a radar lens applicable to a mine environment provided by an embodiment of the present application;

[0075] Figure 2 Installation schematic diagram of a detection camera provided by an embodiment of the present application;

[0076] Figure 3 Block diagram of a device for detecting stains on a radar lens applicable to a mine environment provided by an embodiment of the present application;

[0077] Figure 4 Block diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0078] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0079] The method, device, and equipment for detecting stains on a radar lens applicable to a mine environment according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0080] Figure 1 Schematic flowchart of a method for detecting stains on a radar lens applicable to a mine environment provided by an embodiment of the present application.

[0081] It should be noted that the execution subject of the method for detecting stains on a radar lens applicable to a mine environment according to an embodiment of the present application is the device for detecting stains on a radar lens applicable to a mine environment according to an embodiment of the present application. The device for detecting stains on a radar lens applicable to a mine environment can be configured in an electronic device so that the electronic device can perform the function of detecting stains on a radar lens applicable to a mine environment.

[0082] As shown in Figure 1 the method for detecting stains on a radar lens applicable to a mine environment includes the following steps:

[0083] Step S101, obtaining prior information of the radar scanning window. The prior information includes the curvature and roughness of each point of the radar scanning window in a clean state.

[0084] As an implementation manner, the method for obtaining prior information of the radar scanning window includes:

[0085] Collect data of the radar scanning window in a clean state through a structured light camera to obtain the three-dimensional point cloud data of the clean radar scanning window; based on the three-dimensional point cloud data of the clean radar scanning window, calculate the curvature and roughness of each point on the radar scanning window in the clean state.

[0086] In some implementation manners, a method for calculating the curvature and roughness of each point on the radar scanning window in the clean state based on the three-dimensional point cloud data of the clean radar scanning window; includes: based on the three-dimensional point cloud data of the clean radar scanning window, by using the principal component analysis method for the neighborhood of each point, obtain the curvature of each point; based on the three-dimensional point cloud data of the clean radar scanning window, by performing least squares fitting on the neighborhood of each point, obtain the fitting error, and use this fitting error as the roughness of the corresponding point to obtain the roughness of each point.

[0087] In some implementation manners, after calculating the curvature and roughness of each point on the radar scanning window in the clean state based on the three-dimensional point cloud data of the clean radar scanning window, includes: storing each point on the radar scanning window in the clean state and the curvature and roughness of each point in a multi-dimensional search tree.

[0088] As an implementation manner, obtain prior information through a detection camera, such as Figure 2 As shown, the detection camera 1 includes a fixedly connected infrared camera and a structured light camera, and a lens protection cover 4 is provided outside the lens of the detection camera 1; the detection camera 1 is installed near the radar 2 through a rotatable bracket 3. When the radar 2 is working normally, the detection camera 1 is rotated through the rotatable bracket 3 and placed outside the field of view of the radar 2, and the mechanical lens protection cover 4 is covered; when obtaining prior information, ensure that the radar lens is in a clean state, rotate the rotatable bracket 3 so that the lens of the detection camera 1 faces the radar 2 to collect data. After data collection, retract the detection camera 1 and then cover the lens protection cover 4.

[0089] Exemplarily, when the radar lens is clean, use the detection camera to collect data of the radar, extract the three-dimensional information of the radar scanning window, and statistically analyze the curvature and roughness of each point on the radar scanning window; among them, the curvature is obtained by using the principal component analysis method for the neighborhood of the point; the roughness calculation is performed by performing least squares fitting on the neighborhood of the point, calculating the fitting error, and recording the fitting error as the roughness. For the convenience of searching each point and the curvature and roughness of each point in subsequent steps, store each point on the radar scanning window and its curvature and roughness in a multi-dimensional search tree, such as in a KDTree, denoted as a prior KDTree.

[0090] This step is mainly to obtain prior information and prepare for data comparison in subsequent steps.

[0091] Step S102, obtain the three-dimensional point cloud data and infrared images of the radar scanning window collected by the structured light camera and the infrared camera respectively.

[0092] Exemplarily, at fixed intervals, such as every 2 hours, the detection camera can be adjusted to the front of the radar using a rotatable bracket and the lens protection cover of the detection camera can be opened, and the three-dimensional point cloud data and infrared images of the radar scanning window are collected by the structured light camera and the infrared camera respectively.

[0093] Step S103, convert the infrared image into a binary image, and connect the black regions of the binary image to obtain the region to be detected.

[0094] As an implementation method, a method for converting an infrared image into a binary image and connecting the black regions of the binary image to obtain the region to be detected includes: converting the infrared image into a temperature map; mapping the temperature map into a grayscale map; performing adaptive binarization on the grayscale map to obtain a binary image; and connecting the black regions in the binary image through a region growing algorithm to obtain the region to be detected.

[0095] It can be understood that since the radar scanning window is fixedly connected to the radar and has a relatively high temperature during operation, the region growing algorithm is used to connect the black regions after binarization as the region to be detected.

[0096] Step S104, based on the three-dimensional point cloud data of the radar scanning window, obtain the three-dimensional point cloud data of the region to be detected; and calculate the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected.

[0097] That is to say, obtain the data of the three-dimensional point cloud collected by the structured camera that falls within the region to be detected, and calculate the curvature and roughness of each point in this part of the data. The calculation method of the curvature and roughness of each point is the same as that in step S101 and will not be elaborated here.

[0098] Step S105, based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected and the prior information, determine whether the radar lens is contaminated by stains.

[0099] In some implementations, a method for determining whether a radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and prior information; includes: determining the prior three-dimensional points closest to each point in the area to be detected from the prior information, and obtaining the curvature and roughness of each prior three-dimensional point; calculating the average curvature and average roughness of the area to be detected based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected; calculating the average curvature and average roughness of the prior area based on the curvature and roughness of each prior three-dimensional point; calculating a first difference between the average curvature of the area to be detected and the average curvature of the prior area, and a second difference between the average roughness of the area to be detected and the average roughness of the prior area; determining whether the radar lens is contaminated by stains based on the first difference and the second difference.

[0100] In some implementations, a method for determining whether a radar lens is contaminated by stains based on the first difference and the second difference, includes: determining whether the first difference is greater than the curvature change threshold and whether the second difference is greater than the roughness change threshold; determining that the radar lens is contaminated by stains when the first difference is greater than the curvature change threshold and the second difference is greater than the roughness change threshold.

[0101] Exemplarily, obtain the points closest to each point in the area to be detected in the prior KD tree, denoted as the prior three-dimensional points of the corresponding prior area, and obtain the curvature and roughness of each prior three-dimensional point. Calculate the average curvature and average roughness of the area to be detected, and at the same time calculate the average curvature and average roughness of the corresponding prior area. If the average curvature and average roughness of the area to be detected under normal working conditions are both significantly greater than the average curvature and average roughness of the corresponding prior area, it is considered that the radar scanning surface has been contaminated by stains, otherwise it is considered not to be contaminated for the time being.

[0102] The method for detecting stains on a radar lens applicable to a mine environment in the embodiments of the present application obtains the curvature and roughness of the point cloud data of the radar lens area in the clean state and the curvature and roughness of the point cloud data of the radar lens area during the current detection in advance through a structured camera and an infrared camera, and compares the characteristics of the point cloud data in the clean state with the characteristics of the point cloud data during the current detection, and determines that the radar lens is contaminated by stains according to the data change situation. The method for detecting stains on the radar lens of the present invention can be used for detecting stains on the radar lens in a mine environment, is applicable to any type of radar device, and has universality; once stains are detected, the stains can be removed by manual or automatic cleaning devices in the first time.

[0103] To clearly illustrate the above embodiments, specific examples are provided below. An embodiment of the present application proposes a radar lens stain detection device applicable to a mine environment, including a detection camera, a rotatable bracket, and a processor. The detection camera includes an infrared camera and a structured light camera fixedly connected. A lens protection cover is provided outside the lens of the detection camera. The detection camera is communicatively connected to the processor, and one end of the rotatable bracket is fixedly connected to the detection camera; during detection, the lens of the detection camera faces the radar to be detected. After detection, the rotatable bracket rotates to move the detection camera outside the field of view angle of the radar to be detected;

[0104] The structured light camera and the infrared camera are respectively used to obtain the three-dimensional point cloud data and the infrared image of the radar scanning window of the radar to be detected;

[0105] The processor is used to implement the radar lens stain detection method applicable to the mine environment in the above embodiments based on the three-dimensional point cloud data and the infrared image collected by the detection camera.

[0106] To implement the above embodiments, the present application also proposes a radar lens stain detection device applicable to a mine environment. Figure 3 The block diagram of a radar lens stain detection device applicable to the embodiments of the present application is shown. As Figure 3 shown, the radar lens stain detection device applicable to the mine environment may include: a data processing module 301, a data acquisition module 302, and an image processing module 303.

[0107] Among them, the data processing module 301 is used to obtain the prior information of the radar scanning window. The prior information includes the curvature and roughness of each point of the radar scanning window in the clean state;

[0108] The data acquisition module 302 is used to obtain the three-dimensional point cloud data and the infrared image of the radar scanning window collected by the structured light camera and the infrared camera respectively;

[0109] The image processing module 303 is used to convert the infrared image into a binary image and connect the black areas of the binary image to obtain the area to be detected;

[0110] The data processing module 301 is further used to obtain the three-dimensional point cloud data of the area to be detected based on the three-dimensional point cloud data of the radar scanning window; and calculate the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected;

[0111] The data processing module 301 is further used to determine whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and the prior information.

[0112] Further, in a possible implementation manner of the embodiment of the present application, when the data processing module 301 acquires the prior information of the radar scanning window, it is used for:

[0113] Collect data of the radar scanning window in a clean state through a structured light camera to obtain the three-dimensional point cloud data of the clean radar scanning window;

[0114] Based on the three-dimensional point cloud data of the clean radar scanning window, calculate the curvature and roughness of each point of the radar scanning window in the clean state.

[0115] Further, in a possible implementation manner of the embodiment of the present application, when the data processing module 301 calculates the curvature and roughness of each point of the radar scanning window in the clean state based on the three-dimensional point cloud data of the clean radar scanning window, it is used for:

[0116] Based on the three-dimensional point cloud data of the clean radar scanning window, obtain the curvature of each point by using the principal component analysis method for the neighborhood of each point;

[0117] Based on the three-dimensional point cloud data of the clean radar scanning window, perform least squares fitting on the neighborhood of each point to obtain a fitting error, and use the fitting error as the roughness of the corresponding point to obtain the roughness of each point.

[0118] Further, in a possible implementation manner of the embodiment of the present application, the data processing module 301 is further used for:

[0119] Store each point of the radar scanning window in the clean state and the curvature and roughness of each point in a multi-dimensional search tree.

[0120] Further, in a possible implementation manner of the embodiment of the present application, the image processing module 303 is specifically used for:

[0121] Convert the infrared image into a temperature map;

[0122] Map the temperature map into a grayscale map;

[0123] Obtain a binary image by performing adaptive binarization processing on the grayscale map;

[0124] Connect the black regions in the binary image through a region growing algorithm to obtain the region to be detected.

[0125] Further, in a possible implementation manner of the embodiment of the present application, when the data processing module 301 determines whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the region to be detected and the prior information; it is used for:

[0126] Determine the prior three - dimensional points closest to each point in the area to be detected from the prior information, and obtain the curvature and roughness of each prior three - dimensional point;

[0127] Based on the curvature and roughness of each point in the three - dimensional point cloud data of the area to be detected, calculate the average curvature and average roughness of the area to be detected;

[0128] Based on the curvature and roughness of each prior three - dimensional point, calculate the average curvature and average roughness of the prior area;

[0129] Calculate the first difference between the average curvature of the area to be detected and the average curvature of the prior area, and the second difference between the average roughness of the area to be detected and the average roughness of the prior area;

[0130] Based on the first difference and the second difference, determine whether the radar lens is contaminated by stains.

[0131] Further, in a possible implementation manner of the embodiment of the present application, when the data processing module 301 determines whether the radar lens is contaminated by stains based on the first difference and the second difference, it is used for:

[0132] Judge whether the first difference is greater than the curvature change threshold and whether the second difference is greater than the roughness change threshold;

[0133] In the case where the first difference is greater than the curvature change threshold and the second difference is greater than the roughness change threshold, determine that the radar lens is contaminated by stains.

[0134] It should be noted that the foregoing explanation of the embodiment of the method for detecting stains on the radar lens applicable to the mine environment also applies to the device for detecting stains on the radar lens applicable to the mine environment in this embodiment, and will not be repeated here.

[0135] To implement the above - mentioned embodiment, the present application also proposes an electronic device. Please refer to Figure 4 , Figure 4 is the block diagram of the electronic device provided by the embodiment of the present application. As Figure 4 shown, the electronic device 400 includes: a processor 401, and a memory 402 communicatively connected to the processor 401; the memory 402 stores computer - executable instructions; the processor 401 executes the computer - executable instructions stored in the memory to implement the method provided in the foregoing embodiment.

[0136] To implement the above - mentioned embodiment, the present application also proposes a computer - readable storage medium, in which computer - executable instructions are stored, and when the computer - executable instructions are executed by a processor, they are used to implement the method provided in the foregoing embodiment.

[0137] To implement the above embodiments, the present application further provides a computer program product, including a computer program, which when executed by a processor implements the method provided in the foregoing embodiments.

[0138] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0139] It should be noted that personal information from users should be collected for legal and reasonable purposes and not shared or sold outside of these legal uses. In addition, such collection / sharing should be carried out after obtaining the informed consent of the user, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0140] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the user.

[0141] In the description of the foregoing embodiments, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0142] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0143] Any process or method description represented in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process. The scope of the preferred embodiments of the present application includes additional implementations where functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0144] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise appropriately processing it if necessary, and then storing it in a computer memory.

[0145] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0146] Those of ordinary skill in the art can understand that all or part of the steps carried out in the method of the above embodiments can be completed by instructing relevant hardware through a program. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0147] In addition, each functional unit in various embodiments of the present application can be integrated in a processing module, or each unit can exist physically alone, or two or more units can be integrated in a module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0148] The storage medium mentioned above can be a read-only memory, a magnetic disk, an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for detecting stains on a radar lens applicable to a mine environment, characterized in that, Including the following steps: Obtain prior information of the radar scanning window, where the prior information includes the curvature and roughness of each point of the radar scanning window in a clean state; Obtain the three-dimensional point cloud data and infrared image of the radar scanning window collected by the structured light camera and the infrared camera respectively; Convert the infrared image into a binary image, and connect the black areas of the binary image to obtain the area to be detected; Based on the three-dimensional point cloud data of the radar scanning window, obtain the three-dimensional point cloud data of the area to be detected; and calculate the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected; Based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and the prior information, determine whether the radar lens is contaminated by stains; 2. The method according to claim 1, wherein The obtaining of the prior information of the radar scanning window includes: Collect data of the radar scanning window in a clean state through the structured light camera to obtain the three-dimensional point cloud data of the clean radar scanning window; Based on the three-dimensional point cloud data of the clean radar scanning window, calculate the curvature and roughness of each point of the radar scanning window in a clean state; 3. The method according to claim 2, characterized in that The calculating of the curvature and roughness of each point of the radar scanning window in a clean state based on the three-dimensional point cloud data of the clean radar scanning window includes: Based on the three-dimensional point cloud data of the clean radar scanning window, obtain the curvature of each point by using the principal component analysis method for the neighborhood of each point; Based on the three-dimensional point cloud data of the clean radar scanning window, perform least squares fitting on the neighborhood of each point to obtain the fitting error, and use this fitting error as the roughness of the corresponding point to obtain the roughness of each point; 4. The method according to claim 2, wherein After calculating the curvature and roughness of each point of the radar scanning window in a clean state based on the three-dimensional point cloud data of the clean radar scanning window, it includes: Store each point of the radar scanning window in a clean state and the curvature and roughness of each point in a multi-dimensional search tree; 5. The method according to claim 1, wherein The converting of the infrared image into a binary image and connecting the black areas of the binary image to obtain the area to be detected includes: Convert the infrared image into a temperature map; Map the temperature map into a grayscale map; Obtain a binary image by adaptively binarizing the grayscale map; Connect the black areas in the binary image through a region growing algorithm to obtain the area to be detected; 6. The method according to claim 1, characterized in that, The determining of whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and the prior information includes: Determine the prior three-dimensional points closest to each point of the area to be detected from the prior information, and obtain the curvature and roughness of each prior three-dimensional point; Based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected, calculate the average curvature and average roughness of the area to be detected; Based on the curvature and roughness of each prior three-dimensional point, calculate the average curvature and average roughness of the prior area; Calculate a first difference between the average curvature of the area to be detected and the average curvature of the prior area, and a second difference between the average roughness of the area to be detected and the average roughness of the prior area; Based on the first difference and the second difference, determine whether the radar lens is contaminated by stains.

7. The method according to claim 6, characterized in that, The determining whether the radar lens is contaminated by stains based on the first difference and the second difference includes: Judge whether the first difference is greater than the curvature change threshold and whether the second difference is greater than the roughness change threshold; When the first difference is greater than the curvature change threshold and the second difference is greater than the roughness change threshold, determine that the radar lens is contaminated by stains.

8. A radar lens stain detection device applicable to the mine environment, characterized in that, including: A data processing module, configured to obtain prior information of a radar scanning window, where the prior information includes the curvature and roughness of each point of the radar scanning window in a clean state; A data acquisition module, configured to obtain three-dimensional point cloud data and infrared images of the radar scanning window respectively collected by a structured light camera and an infrared camera; An image processing module, configured to convert the infrared image into a binary image, and connect the black areas of the binary image to obtain an area to be detected; The data processing module is further configured to obtain three-dimensional point cloud data of the area to be detected based on the three-dimensional point cloud data of the radar scanning window; and calculate the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected; The data processing module is further configured to determine whether the radar lens is contaminated by stains based on the curvature and roughness of each point in the three-dimensional point cloud data of the area to be detected and the prior information.

9. A radar lens stain detection device applicable to the mine environment, characterized in that, Including a detection camera, a rotatable bracket and a processor, the detection camera includes an infrared camera and a structured light camera fixedly connected, a lens protection cover is provided outside the lens of the detection camera, the detection camera is communicatively connected to the processor, and one end of the rotatable bracket is fixedly connected to the detection camera; during detection, the lens of the detection camera faces the radar to be detected, and after detection, the rotatable bracket moves the detection camera out of the field of view angle of the radar to be detected by rotation; The structured light camera and the infrared camera are respectively configured to obtain three-dimensional point cloud data and infrared images of a radar scanning window of the radar to be detected; The processor is configured to implement the method according to any one of claims 1-7 based on the three-dimensional point cloud data and infrared images collected by the detection camera.

10. A computer-readable storage medium, characterized in that, Computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1-7.

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