Hail detection method, device, computer equipment and readable storage medium

Abstract

The present application relates to a hail detection method, device, computer equipment and readable storage medium. The method comprises: obtaining a background image corresponding to a target area, wherein the background image is generated based on at least one historical real-scene image corresponding to the target area that does not include hail; obtaining at least one target real-scene image corresponding to the target area, and comparing the target real-scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real-scene image relative to the background image; according to the comparison result, obtaining a hail detection result of the target area, wherein the hail detection result is used to characterize the presence of hail in the target area or the absence of hail in the target area. The use of this method can reduce the cost of hail detection, is easy to implement, and improves the universality of hail detection.

Description

Technical Field

[0001] The present application relates to the field of meteorological detection technology, and in particular to a hail detection method, device, computer equipment and readable storage medium. Background Art

[0002] Hail is a weather phenomenon, which is ice formed by rising water vapor encountering a sudden drop in temperature. It is most common in summer or at the turn of spring and summer. Hail has strong destructive power. Hail often damages houses, vehicles, destroys farmland, trees, and injures people or livestock. It is one of the serious natural disasters.

[0003] At present, the mainstream method for detecting hail is to deploy dual-polarization Doppler weather radar, transmit horizontal and vertical polarization signals through the radar's transmitter, and process the data in parallel through the radar's dual-channel receiver and digital intermediate frequency. Specific algorithms (such as the United States' NEX-TRAD hail detection algorithm) are used in the processing process to detect hail.

[0004] However, the above-mentioned method for detecting hail requires the deployment of dual-polarization Doppler weather radars, which have high radar deployment costs, and hail detection is restricted by the implementation authority of specific algorithms, resulting in poor universality of hail detection, for example, it is not applicable to some individual users. Therefore, providing a low-cost and easy-to-implement hail detection method has become an urgent problem to be solved. Summary of the invention

[0005] Based on this, it is necessary to provide a low-cost and easy-to-implement hail detection method, device, computer equipment and readable storage medium to address the above technical problems.

[0006] In a first aspect, an embodiment of the present application provides a hail detection method, the method comprising:

[0007] Acquire a background image corresponding to the target area, where the background image is generated based on at least one historical real scene image corresponding to the target area that does not include hail;

[0008] Acquire at least one target real scene image corresponding to the target area, and compare the target real scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real scene image relative to the background image;

[0009] A hail detection result of the target area is acquired according to the comparison result, and the hail detection result is used to indicate whether hail exists in the target area or does not exist in the target area.

[0010] In one of the embodiments, the at least one target real scene image is a plurality of target real scene images, and each of the target real scene images is continuous in time sequence.

[0011] In one embodiment, the background image includes a reference background pixel area, and the comparing the target real scene image with the background image to obtain a comparison result includes:

[0012] For each of the target real scene images, determining a target pixel area corresponding to the reference background pixel area from the target real scene image;

[0013] Subtract the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area to obtain a subtraction result;

[0014] The difference result of each of the target real scene images is determined as the comparison result.

[0015] In one embodiment, obtaining the hail detection result of the target area according to the comparison result includes:

[0016] For each of the target real scene images, detecting whether the difference result of each pixel point in the target pixel area in the target real scene image is greater than a preset difference threshold;

[0017] Determine, from each pixel point of the target pixel area, a plurality of target pixel points whose difference result is greater than the preset difference threshold, and generate a mask map corresponding to the target real scene image according to the plurality of target pixel points, wherein the mask map includes a plurality of mask areas formed by the plurality of target pixel points;

[0018] Detecting whether the attribute information of the plurality of mask regions satisfies a preset attribute condition, wherein the preset attribute condition is related to an area of ​​each of the mask regions and a number of the plurality of mask regions;

[0019] If the attribute information of multiple mask areas of at least one target real scene image satisfies the preset attribute condition, it is determined that the hail detection result is that hail exists in the target area.

[0020] In one embodiment, after obtaining the hail detection result of the target area according to the comparison result, the method further includes:

[0021] If the hail detection result indicates that hail exists in the target area, obtaining at least one updated real scene image corresponding to the target area, and verifying the accuracy of the hail detection result based on the at least one updated real scene image;

[0022] When the hail detection result passes the accuracy verification, the hail detection result is output.

[0023] In one embodiment, after obtaining the hail detection result of the target area according to the comparison result, the method further includes:

[0024] If the hail detection result indicates that hail exists in the target area, obtaining environmental data corresponding to the target area, wherein the environmental data includes at least one of a temperature and air humidity corresponding to the target area;

[0025] Detecting whether the environmental data meets the environmental conditions for hail generation;

[0026] If the environmental data meets the environmental conditions for hail generation, the hail detection result is output.

[0027] In one embodiment, after obtaining the hail detection result of the target area according to the comparison result, the method further includes:

[0028] If the hail detection result indicates that hail exists in the target area, measuring the current distance information between the distance measuring component and a preset target object in the target area by a distance measuring component;

[0029] Detecting whether a difference between the current distance information and reference distance information corresponding to the target area is greater than a preset distance threshold, wherein the reference distance information is distance information between the distance measuring component and the preset target object measured by the distance measuring component when no hail exists in the target area;

[0030] If the difference between the current distance information and the reference distance information is greater than the preset distance threshold, the hail detection result is output.

[0031] In one embodiment, after obtaining the hail detection result of the target area according to the comparison result, the method further includes:

[0032] If the hail detection result indicates that hail exists in the target area, sending hail prompt information to the user terminal, where the hail prompt information is used to prompt that hail exists in the target area;

[0033] If a video viewing request sent by the user terminal is received, hail video data corresponding to the target area is acquired, and the hail video data is sent to the user terminal.

[0034] In a second aspect, an embodiment of the present application provides a hail detection device, the device comprising:

[0035] A first acquisition module is used to acquire a background image corresponding to a target area, wherein the background image is generated based on at least one historical real scene image corresponding to the target area that does not include hail;

[0036] a comparison module, configured to obtain at least one target real scene image corresponding to the target area, and compare the target real scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real scene image relative to the background image;

[0037] The second acquisition module is used to acquire a hail detection result of the target area according to the comparison result, wherein the hail detection result is used to indicate whether hail exists in the target area or does not exist in the target area.

[0038] In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method of the first aspect when executing the computer program.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of the first aspect described above.

[0040] The beneficial effects brought by the technical solution provided by the embodiment of the present application include at least:

[0041] By acquiring a background image corresponding to a target area, the background image is generated based on at least one historical real-scene image corresponding to the target area that does not include hail, and acquiring at least one target real-scene image corresponding to the target area, and comparing the target real-scene image with the background image to obtain a comparison result, the comparison result includes pixel value change information of the target real-scene image relative to the background image, and then acquiring a hail detection result of the target area according to the comparison result, the hail detection result is used to characterize the presence of hail in the target area or the absence of hail in the target area; thus, after the target real-scene image of the target area is compared with the background image to obtain a comparison result including pixel value change information of the target real-scene image relative to the background image, it can be determined whether hail exists in the target area according to the comparison result; the method avoids the need to deploy a dual-polarization Doppler weather radar and combine a specific algorithm (such as the NEX-TRAD hail detection algorithm) to detect hail in the traditional technology, and the disadvantage that the hail detection has poor universality due to the high cost of radar deployment and the need to obtain corresponding authorization for the hail detection algorithm, especially for some individual users. The embodiment of the present application only needs to acquire image data without deploying radar during hail detection, thereby reducing the cost of hail detection. The hail detection process does not need to be combined with specific hail detection algorithms such as NEX-TRAD, so there is no need to obtain the implementation authority of these specific algorithms. This makes implementation easy and improves the universality of hail detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 1 is a flow chart of a hail detection method in one embodiment;

[0043] Figure 2 It is a schematic diagram of some detailed steps of step S200 in another embodiment;

[0044] Figure 3 is a schematic diagram of detailed steps of step S300 in another embodiment;

[0045] Figure 4 is a flow chart of a hail detection method in another embodiment;

[0046] Figure 5 is a flow chart of a hail detection method in another embodiment;

[0047] Figure 6 is a flow chart of a hail detection method in another embodiment;

[0048] Figure 7 Schematic diagram of a hail detection system in one embodiment.

[0049] Figure 8 is a structural block diagram of a hail detection device in one embodiment;

[0050] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] The hail detection method, device, computer equipment, and readable storage medium provided in the embodiments of the present application are intended to solve the technical problem of poor universality of the hail detection method in the traditional technology due to the high cost of radar deployment and the need to obtain corresponding authorization for a specific hail detection algorithm. The technical solution of the present application will be described in detail below through embodiments and in conjunction with the accompanying drawings. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0053] It should be noted that the execution subject of the hail detection method provided in the embodiment of the present application may be a hail detection device, which may be implemented as part or all of a computer device through software, hardware, or a combination of software and hardware, and the computer device may be a server. In the following method embodiments, the execution subject is a computer device as an example for explanation. It is understandable that the hail detection method provided in the following method embodiments may also be applied to a terminal, and may also be applied to a system including a terminal and a server, and may be implemented through the interaction between the terminal and the server.

[0054] In one embodiment, Figure 1 As shown, a hail detection method is provided, comprising the following steps:

[0055] Step S100, obtaining a background image corresponding to the target area.

[0056] In the embodiment of the present application, the target area may be an area where hail detection is required, for example, a specific road, a user's residential area, a farmland, etc. The computer device obtains a background image corresponding to the target area, and the background image may be generated by the computer device based on at least one historical real-life image corresponding to the target area that does not include hail. As an implementation method, after the computer device generates the background image, the background image may be stored in a database of the computer device corresponding to the target area. When hail detection is required for the target area, the computer device obtains the background image from the database. The background image is used as a reference background when hail does not exist in the target area during the hail detection process.

[0057] As an implementation method, the computer device can collect at least one historical real-life image that does not include hail corresponding to the target area through the collection component at a historical moment. For example, in a historical sunny weather environment, the computer device controls the collection component to collect multiple historical real-life images of the target area, and the sampling moments corresponding to the multiple historical real-life images can be continuous. Since hail is mostly white, when the collection component is deployed, the collection screen can be fixedly aimed at a fixed building or other non-white background in the target area. The fixed building or non-white background can form a significant color difference with the hail. In this way, when the computer device uses the background image generated based on multiple historical real-life images as a reference background, it is beneficial for the computer device to visually separate the hail according to the color difference.

[0058] The computer device generates a background image based on multiple historical real-scene images. In one possible implementation, the computer device can call the cv2.Canny function in the OpenCV vision library to extract contours from the multiple historical real-scene images, and then call the cv2.findContours function to find connected domains (i.e., the areas enclosed by line segments connected end to end) from the extracted contours and call the cv2.contourArea function to obtain the area of ​​each connected domain, and then call the cv2.drawContours function to fill the small-area connected domains, and call the cv2.getStructuringElement function and the cv2.dilate function to perform regional expansion so that the smaller connected domains are connected into a whole to form a background, thereby obtaining a background image.

[0059] Since the multiple historical real-scene images are continuous in time sequence, that is, the lighting and other environments are similar, the computer device analyzes the unchanged pixels of the multiple historical real-scene images through the above process, thereby extracting the background image, which has the same size as the historical real-scene image. As an implementation method, taking the example that the acquisition component fixes the acquisition screen to a fixed building in the target area when deployed, the background image includes a reference background pixel area corresponding to the fixed building, and the pixel values ​​of other areas in the background image except the reference background pixel area are all 0 (that is, black).

[0060] As time goes by, the acquisition components may become loose or the environment may change, which may cause a large difference between the background image stored in the computer device and the current actual environment. In order to reduce this difference, the computer device in the embodiment of the present application can periodically perform the above-mentioned process of generating a background image based on multiple historical real-scene images on the target area based on a preset time period, and update the stored background image, thereby ensuring the accuracy of the background image, which is conducive to improving the accuracy of the hail detection method.

[0061] In the embodiment of the present application, the acquisition component can be a high-sensitivity imager. Due to its high-sensitivity characteristics, the high-sensitivity imager can acquire images similar to daytime imaging effects even at night or in poor lighting conditions, thereby realizing all-weather detection of hail, expanding the time range of hail detection, and helping to improve the reliability of hail detection.

[0062] Step S200: acquiring at least one target real scene image corresponding to the target area, and comparing the target real scene image with the background image to obtain a comparison result.

[0063] The computer device collects at least one target real scene image corresponding to the target area through the collection component. In one possible implementation, the at least one target real scene image may be one target real scene image; in another possible implementation, the at least one target real scene image may also be a plurality of target real scene images, each of which is continuous in time sequence. For example, the computer device may collect the target real scene image at the current sampling moment and the target real scene images at two consecutive sampling moments before the current sampling moment, thereby obtaining three continuous target real scene images.

[0064] It should be noted that, compared with a single target real scene image, multiple target real scene images that are continuous in time can more accurately represent the real scene of the target area, that is, the current actual environment. The computer device uses multiple target real scene images that are continuous in time for hail detection, which can avoid using only one target real scene image, and if there is interference in the one target real scene image, which reduces the accuracy of hail detection, it is beneficial to improve the accuracy of hail detection. In the embodiments of the present application, the at least one target real scene image is taken as an example to illustrate the implementation of the embodiments of the present application. Of course, in other embodiments, the computer device can also obtain a target real scene image corresponding to the target area, and compare the one target real scene image with the background image to obtain a comparison result including the pixel value change information of the target real scene image relative to the background image, and obtain the hail detection result of the target area based on the comparison result, without specific limitation here.

[0065] Since the acquisition screen of the acquisition component is fixed, the screen field of view and size of the target real scene image and the historical real scene image are the same. The computer device compares each target real scene image with the background image respectively, which can be to compare the pixel value of each pixel point of the target real scene image with the pixel value of each pixel point of the background image. For example, for a target real scene image, the computer device can subtract the pixel value of each pixel point of the target real scene image from the pixel value of the pixel point at the corresponding position in the background image, so as to obtain the pixel value change information of the target real scene image relative to the background image. The computer device compares each target real scene image with the background image respectively to obtain a comparison result, which includes the pixel value change information of each target real scene image relative to the background image.

[0066] Step S300: Obtain hail detection results in the target area according to the comparison results.

[0067] The hail detection result is used to indicate whether hail exists in the target area or not. The computer device determines whether hail exists in the target area based on the comparison result.

[0068] In a possible implementation, a computer device may mark pixel points in a target real scene image whose pixel values ​​are significantly different from those of a background image, and determine whether hail exists in the target area based on the size and density of the area formed by these marked pixel points; for example, if there are areas formed by a large number of marked pixel points in each target real scene image, and the area of ​​the formed area matches the size ratio of the hail, it can be determined that hail exists in the target area.

[0069] In another possible implementation, since hail is mostly white, if there are areas formed by a large number of marked pixels in each target real-scene image, and the area of ​​the formed area matches the size ratio of the hail, the computer device can also detect whether the color represented by the actual pixel values ​​of these marked pixels in the target real-scene image is white or close to white. If so, it is further determined that hail exists in the target area.

[0070] In the embodiment of the present application, as an implementation method, the computer device can be a microcomputer such as Raspberry Pi. The cost of a microcomputer is relatively low, which can further reduce the cost of the hail detection method.

[0071] In this embodiment, a background image corresponding to the target area is obtained, where the background image is generated based on at least one historical real-scene image corresponding to the target area that does not include hail, and at least one target real-scene image corresponding to the target area is obtained, and the target real-scene image is compared with the background image to obtain a comparison result, where the comparison result includes pixel value change information of the target real-scene image relative to the background image, and then a hail detection result of the target area is obtained according to the comparison result, where the hail detection result is used to characterize the presence of hail in the target area or the absence of hail in the target area. Thus, after the target real-scene image of the target area is compared with the background image to obtain a comparison result including pixel value change information of the target real-scene image relative to the background image, it can be determined whether hail exists in the target area according to the comparison result. This avoids the need to deploy a dual-polarization Doppler weather radar and combine a specific algorithm (such as the NEX-TRAD hail detection algorithm) to detect hail in the traditional technology. Due to the high cost of radar deployment and the need to obtain corresponding authorization for the hail detection algorithm, the universality of hail detection is poor, especially for some individual users. The embodiment of the present application only needs to acquire image data without deploying radar during hail detection, thereby reducing the cost of hail detection. The hail detection process does not need to be combined with specific hail detection algorithms such as NEX-TRAD, so there is no need to obtain the implementation authority of these specific algorithms. This makes implementation easy and improves the universality of hail detection.

[0072] In one embodiment, in the above Figure 1 Based on the embodiment shown, see Figure 2This embodiment relates to a process of how a computer device compares each target real scene image with a background image to obtain a comparison result when at least one target real scene image is a plurality of target real scene images. Figure 2 As shown, the process includes step S201, step S202 and step S203:

[0073] Step S201 : for each target real scene image, determine a target pixel area corresponding to a reference background pixel area from the target real scene image.

[0074] In the embodiment of the present application, the background image includes a reference background pixel area. The reference background pixel area corresponds to the background in the target area. For example, when the acquisition component is deployed, the acquisition image is fixedly aimed at a fixed building in the target area, and the reference background pixel area is the area corresponding to the fixed building in the background image. The pixel value of the reference background pixel area in the background image is the real pixel value, and the pixel values ​​of other areas in the background image except the reference background pixel area are all 0.

[0075] Since the acquisition screen of the acquisition component is fixed, the target real scene image and the background image have the same size, and the area in the target real scene image corresponding to the reference background pixel area of ​​the background image is the target pixel area. For each target real scene image, the computer device determines the target pixel area from the target real scene image, and the area in the target real scene image that has the same position as the reference background pixel area can be determined as the target pixel area.

[0076] In another possible implementation, for each target real scene image, the computer device can also detect whether the pixel value of each pixel point in the background image is 0. If it is 0, it is determined that the pixel point at that position in the background image is not in the reference background pixel area. Therefore, it is determined that the pixel point at that position in the target real scene image is not in the target pixel area. Similarly, if it is not 0, it is determined that the pixel point at that position in the target real scene image is a pixel point in the target pixel area, so that by detecting each pixel point in the target real scene image in turn, the target pixel area in the target real scene image can be determined.

[0077] Step S202 , subtracting the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area to obtain a subtraction result.

[0078] In the embodiment of the present application, the computer device compares the target real scene image with the background image, which may be to subtract the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding position pixel point in the reference background pixel area to obtain a subtraction result.

[0079] It can be understood that the computer device only performs a difference comparison between the target real scene image and part of the background image (the target pixel area and the reference background pixel area), which can improve the comparison efficiency, thereby shortening the hail detection time, facilitating real-time detection of hail, and saving computing resources of the computer device.

[0080] Step S203: Determine the difference result of each target real scene image as the comparison result.

[0081] The computer device determines the difference result of each target real scene image as the comparison result.

[0082] based on Figure 2 The embodiment shown, correspondingly, see Figure 3 , step S300 may include step S301, step S302, step S303 and step S304:

[0083] Step S301 : for each target real scene image, detecting whether the difference result of each pixel point in the target pixel area in the target real scene image is greater than a preset difference threshold.

[0084] For each target real scene image, the computer device detects whether the difference result of each pixel point in the target pixel area in the target real scene image is greater than a preset difference threshold. It is understandable that, when there is no hail in the target area, since the acquisition screen of the acquisition component is fixed, the pixel values ​​of the pixel points at the corresponding positions in the target real scene image and the background image should be the same or similar.

[0085] Step S302 , determining a plurality of target pixel points whose difference results are greater than a preset difference threshold from the pixel points in the target pixel area, and generating a mask image corresponding to the target real scene image according to the plurality of target pixel points.

[0086] If there are pixels in the target pixel area whose difference results are greater than the preset difference threshold, it indicates that there are variation factors in the target pixel area, such as the imaging of the falling hail process, which belongs to the variation factor. The computer device can determine the pixels whose difference results are greater than the preset difference threshold as target pixels, and mark each target pixel in the target real scene image, and set the pixel values ​​of other pixels in the target real scene image except the target pixel to 0, so as to obtain the mask image corresponding to the target real scene image. The mask image includes multiple mask areas formed by multiple target pixels.

[0087] Step S303: detecting whether the attribute information of the plurality of mask regions meets a preset attribute condition.

[0088] In the embodiment of the present application, the preset attribute condition is related to the area of ​​each mask area and the number of multiple mask areas. The computer device can set the preset attribute condition according to the characteristics of the hail falling process. For example, the preset attribute condition can be set as the area of ​​the mask area is within the area range of the hail imaging area, the number of mask areas is not less than the minimum number of hail falling, etc.

[0089] The computer device detects whether the attribute information of multiple mask areas in the mask map corresponding to each target real scene image meets the preset attribute conditions, for example, it can detect whether the area of ​​each mask area is within the area range of the hail imaging area, and whether the number of mask areas included in the mask map is greater than the minimum number of hail falling. As an implementation method, for the mask map, the computer device can call the cv2.findContours function in the OpenCV vision library to find the connected domain, that is, the mask area, and then obtain the area of ​​each mask area by calling the cv2.contourArea function.

[0090] Step S304: if the attribute information of multiple mask areas of at least one target real scene image satisfies a preset attribute condition, it is determined that the hail detection result is that hail exists in the target area.

[0091] If, among the multiple target real scene images, the attribute information of multiple mask areas of at least one target real scene image satisfies the preset attribute condition, the computer device determines that the hail detection result of the target area is that hail exists in the target area. That is, if there are more mask areas in the mask image corresponding to at least one target real scene image, and the area of ​​the mask area is within the area range of the hail imaging area, the hail detection result is determined to be that hail exists in the target area. If the attribute information of multiple mask areas of multiple consecutive target real scene images all meet the preset attribute condition, the hail detection result is also determined to be that hail exists in the target area, and in this case, the possibility of hail existing in the target area is greater.

[0092] In a possible implementation, after determining that hail exists in the target area, the computer device may also determine the hail intensity according to the area of ​​each mask area in each target real scene image and the number of multiple mask areas. As an implementation, the computer device may store a mapping relationship between the area interval of the mask area, the number interval of the mask area, and the hail intensity. The computer device may obtain the average area of ​​each mask area and the number of mask areas in the mask image corresponding to the target real scene image, and then search for the corresponding area interval of the mask area and the number interval of the mask area in the mapping relationship according to the average area and the number of mask areas, so as to determine the corresponding hail intensity.

[0093] This embodiment improves the comparison efficiency by subtracting the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area, thereby shortening the hail detection time, facilitating the real-time detection of hail, and saving the computing resources of the computer equipment. After determining that hail exists in the target area, this embodiment further obtains the hail intensity in the target area, and can provide the user with more refined hail detection data, thereby more effectively helping the user to make a timely anti-hail response, which is beneficial to reducing the user's loss and ensuring the user's safety.

[0094] In the above Figure 1 Based on the embodiment shown, see Figure 4 This embodiment is a process of verifying the accuracy of hail detection results by a computer device in one embodiment. Figure 4 As shown, the hail detection method of this embodiment further includes step S401 and step S402:

[0095] Step S401: If the hail detection result indicates that hail exists in the target area, at least one updated real scene image corresponding to the target area is obtained, and the accuracy of the hail detection result is verified based on the at least one updated real scene image.

[0096] In an embodiment of the present application, after the computer device determines that the hail detection result of the target area is that hail exists in the target area, the computer device obtains at least one updated real-scene image corresponding to the target area, and verifies the accuracy of the hail detection result based on the at least one updated real-scene image.

[0097] In one possible implementation, the at least one updated real scene image may be one updated real scene image; in another possible implementation, the at least one updated real scene image may be a plurality of updated real scene images, each updated real scene image is continuous in time sequence, and the sampling interval between each updated real scene image is smaller than the sampling interval between each target real scene image.

[0098] As an implementation mode, taking at least one updated real scene image as multiple updated real scene images as an example, after the computer device determines that the hail detection result of the target area is that hail exists in the target area, it can reduce the sampling interval and control the acquisition component to perform continuous image sampling on the target area again to obtain multiple updated real scene images. The computer device can adopt the following method: Figure 2 and Figure 3 In the illustrated embodiment, each updated real scene image is compared with the target image, and whether hail exists in the target area is detected based on the comparison result.

[0099] It should be noted that the implementation method of the computer device detecting whether hail exists in the target area based on multiple updated real-scene images and background images is the same as or similar to the implementation method of the computer device detecting whether hail exists in the target area based on multiple target real-scene images and background images. Please refer to the above embodiments for details, and no further details will be given here.

[0100] Step S402: when the hail detection result passes the accuracy verification, output the hail detection result.

[0101] If the computer device determines that hail exists in the target area based on at least one updated real-scene image, it determines that the hail detection result passes the accuracy verification. If the hail detection result passes the accuracy verification, the hail detection result is output, for example, a push message may be sent to the user terminal to prompt that hail has occurred in the target area, so as to remind the user to take timely anti-hail response to reduce user losses and ensure user safety.

[0102] In the above Figure 1 Based on the embodiment shown, see Figure 5 This embodiment is a process of verifying the accuracy of hail detection results by a computer device in another embodiment. Figure 5 As shown, the hail detection method of this embodiment further includes step S501, step S502 and step S503:

[0103] Step S501: If the hail detection result indicates that hail exists in the target area, then environmental data corresponding to the target area is obtained.

[0104] In an embodiment of the present application, after the computer device determines that the hail detection result in the target area is that hail exists in the target area, the computer device can obtain environmental data corresponding to the target area through a sensor deployed in the target area, and the environmental data includes at least one of the temperature and air humidity corresponding to the target area. The sensor can be a temperature and humidity sensor.

[0105] Step S502, detecting whether the environmental data meets the environmental conditions for hail generation.

[0106] The computer device detects whether the environmental data meets the environmental conditions for hail generation, and the environmental conditions may be associated with meteorological factors for hail generation, for example, the necessary humidity for hail generation may be used as a humidity threshold and the necessary temperature for hail generation may be used as a temperature threshold. The computer device may then detect whether the temperature included in the environmental data is lower than the temperature threshold and whether the humidity included in the environmental data is higher than the humidity threshold.

[0107] Step S503: if the environmental data meets the environmental conditions for hail generation, the hail detection result is output.

[0108] If the environmental data meets the environmental conditions for hail formation, the computer device determines that the current environment in the target area is prone to hail formation, and further determines that the hail detection result is accurate, and then outputs the hail detection result, which can ensure the reliability of the hail detection result.

[0109] In the above Figure 1 Based on the embodiment shown, see Figure 6 This embodiment is a process of verifying the accuracy of hail detection results by a computer device in another embodiment. Figure 6 As shown, the hail detection method of this embodiment further includes step S601, step S602 and step S603:

[0110] Step S601: if the hail detection result indicates that hail exists in the target area, the current distance information between the distance measuring component and a preset target object in the target area is measured by the distance measuring component.

[0111] If the computer device detects that the hail detection result in the target area is that hail exists in the target area, in order to improve the reliability of the hail detection result, the computer device obtains the current distance information between the distance measuring component and the preset target object in the target area through the distance measuring component.

[0112] In the embodiment of the present application, the distance measuring component may be a laser distance measuring sensor, which is directed toward a preset target in the target area and emits a laser beam to obtain the laser detection distance, that is, the current distance information. It is understandable that if hail exists in the target area, the laser beam emitted by the laser distance measuring sensor may be blocked by the hail before reaching the preset target, resulting in a shorter round-trip transmission path of the laser, thereby shortening the distance. The preset target may be any target in the target area.

[0113] Step S602: Detect whether the difference between the current distance information and the reference distance information corresponding to the target area is greater than a preset distance threshold.

[0114] The computer device detects whether the difference between the current distance information and the reference distance information corresponding to the target area is greater than a preset distance threshold, wherein the reference distance information is the distance information between the distance measuring component and the preset target object measured by the distance measuring component when there is no hail in the target area, that is, the actual distance between the distance measuring component and the preset target object. The preset distance threshold can be set by itself during implementation, for example, it can be set based on manual experience.

[0115] Step S603: if the difference between the current distance information and the reference distance information is greater than a preset distance threshold, the hail detection result is output.

[0116] If the computer device detects that the difference between the current distance information and the reference distance information corresponding to the target area is greater than a preset distance threshold, it indicates that the distance change is too large, and then it is further determined that hail exists in the target area, and then the hail detection result indicating that hail exists in the target area is output, thereby ensuring the reliability of the hail detection result.

[0117] In one embodiment, based on the above embodiment, the hail detection method of this embodiment may further include step A1 and step A2:

[0118] Step A1: If the hail detection result indicates that hail exists in the target area, hail prompt information is sent to the user terminal, where the hail prompt information is used to prompt that hail exists in the target area.

[0119] Step A2: if a video viewing request sent by a user terminal is received, hail video data corresponding to the target area is obtained, and the hail video data is sent to the user terminal.

[0120] In one possible implementation, see Figure 7 , Figure 7 Schematic diagram of a hail detection system in an embodiment. Taking the computer device as Raspberry Pi as an example, Raspberry Pi communicates with the user terminal (Phone) through a communication module (GSM / HLS), and Raspberry Pi controls a high-sensitivity imager (Camera) to collect historical real-scene images, target real-scene images, and updated real-scene images of the target area, and collects environmental data of the target area through sensors (Sensors), such as temperature and humidity sensors, and laser range sensors collect laser detection distances of the target area.

[0121] If the Raspberry Pi detects hail in the target area, it will send a hail prompt message to the user terminal. The hail prompt message may carry a query confirmation option, such as "Do you want to watch the video?" If the user clicks "Yes", the user terminal will send a video viewing request to the Raspberry Pi. After receiving the video viewing request sent by the user, the Raspberry Pi controls the high-sensitivity imager to collect the hail video data corresponding to the target area, and sends the hail video data to the user terminal. This helps users to understand hail information in real time and more intuitively, such as the size and intensity of hail, and avoid or reduce the adverse effects of hail on the safety of users' lives and property, as well as their daily lives and travel.

[0122] It should be understood that although Figure 1-6 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-6At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0123] In one embodiment, Figure 8 As shown, a hail detection device is provided, comprising:

[0124] A first acquisition module 10 is used to acquire a background image corresponding to a target area, wherein the background image is generated based on at least one historical real scene image corresponding to the target area that does not include hail;

[0125] A comparison module 20 is used to obtain at least one target real scene image corresponding to the target area, and compare the target real scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real scene image relative to the background image;

[0126] The second acquisition module 30 is used to acquire a hail detection result of the target area according to the comparison result, wherein the hail detection result is used to indicate whether hail exists in the target area or does not exist in the target area.

[0127] Optionally, the at least one target real scene image is a plurality of target real scene images, and each of the target real scene images is continuous in time sequence.

[0128] Optionally, the background image includes a reference background pixel area, and the comparison module 20 includes:

[0129] A first determining unit, configured to determine, for each of the target real scene images, a target pixel area corresponding to the reference background pixel area from the target real scene image;

[0130] A calculation unit, used for subtracting the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area to obtain a subtraction result;

[0131] The second determining unit is used to determine the difference result of each of the target real scene images as the comparison result.

[0132] Optionally, the second acquisition module 30 includes:

[0133] A first detection unit is used to detect, for each of the target real scene images, whether a difference result of each pixel point in a target pixel area in the target real scene image is greater than a preset difference threshold;

[0134] A mask map generating unit, configured to determine, from the pixels of the target pixel area, a plurality of target pixel points whose difference results are greater than the preset difference threshold, and generate a mask map corresponding to the target real scene image according to the plurality of target pixel points, wherein the mask map includes a plurality of mask areas formed by the plurality of target pixel points;

[0135] A second detection unit, configured to detect whether the attribute information of the plurality of mask regions satisfies a preset attribute condition, wherein the preset attribute condition is related to an area of ​​each of the mask regions and a number of the plurality of mask regions;

[0136] The third determining unit is configured to determine that the hail detection result is that hail exists in the target area if the attribute information of multiple mask areas of at least one target real scene image meets the preset attribute condition.

[0137] Optionally, the device further comprises:

[0138] a verification module, configured to obtain at least one updated real scene image corresponding to the target area if the hail detection result indicates that hail exists in the target area, and verify the accuracy of the hail detection result based on the at least one updated real scene image;

[0139] The first output module is used to output the hail detection result if the hail detection result passes the accuracy verification.

[0140] Optionally, the device further comprises:

[0141] A third acquisition module is used for acquiring environmental data corresponding to the target area if the hail detection result indicates that hail exists in the target area, wherein the environmental data includes at least one of a temperature and air humidity corresponding to the target area;

[0142] A first detection module, used to detect whether the environmental data meets the environmental conditions for hail generation;

[0143] The second output module is used to output the hail detection result if the environmental data meets the environmental conditions for hail generation.

[0144] Optionally, the device further comprises:

[0145] a fourth acquisition module, configured to measure, by means of a distance measuring component, current distance information between the distance measuring component and a preset target object in the target area if the hail detection result indicates that hail exists in the target area;

[0146] A second detection module is used to detect whether a difference between the current distance information and reference distance information corresponding to the target area is greater than a preset distance threshold, wherein the reference distance information is distance information between the distance measuring component and the preset target object measured by the distance measuring component when there is no hail in the target area;

[0147] The third output module is used to output the hail detection result if the difference between the current distance information and the reference distance information is greater than the preset distance threshold.

[0148] Optionally, the device further comprises:

[0149] a prompting module, configured to send hail prompting information to a user terminal if the hail detection result indicates that hail exists in the target area, wherein the hail prompting information is used to prompt that hail exists in the target area;

[0150] The sending module is used to obtain the hail video data corresponding to the target area and send the hail video data to the user terminal if a video viewing request sent by the user terminal is received.

[0151] The specific definition of the hail detection device can be found in the definition of the hail detection method above, which will not be repeated here. Each module in the above hail detection device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0152] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data of the hail detection method. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hail detection method is implemented.

[0153] Those skilled in the art will understand that Fig. 9The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0154] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0155] Acquire a background image corresponding to the target area, where the background image is generated based on at least one historical real scene image corresponding to the target area that does not include hail;

[0156] Acquire at least one target real scene image corresponding to the target area, and compare the target real scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real scene image relative to the background image;

[0157] A hail detection result of the target area is acquired according to the comparison result, and the hail detection result is used to indicate whether hail exists in the target area or does not exist in the target area.

[0158] In one embodiment, the at least one target real scene image is a plurality of target real scene images, and each of the target real scene images is continuous in time sequence.

[0159] In one embodiment, the background image includes a reference background pixel area, and the processor further implements the following steps when executing the computer program:

[0160] For each of the target real scene images, determining a target pixel area corresponding to the reference background pixel area from the target real scene image;

[0161] Subtract the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area to obtain a subtraction result;

[0162] The difference result of each of the target real scene images is determined as the comparison result.

[0163] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0164] For each of the target real scene images, detecting whether the difference result of each pixel point in the target pixel area in the target real scene image is greater than a preset difference threshold;

[0165] Determine, from each pixel point of the target pixel area, a plurality of target pixel points whose difference result is greater than the preset difference threshold, and generate a mask map corresponding to the target real scene image according to the plurality of target pixel points, wherein the mask map includes a plurality of mask areas formed by the plurality of target pixel points;

[0166] Detecting whether the attribute information of the plurality of mask regions satisfies a preset attribute condition, wherein the preset attribute condition is related to an area of ​​each of the mask regions and a number of the plurality of mask regions;

[0167] If the attribute information of multiple mask areas of at least one target real scene image satisfies the preset attribute condition, it is determined that the hail detection result is that hail exists in the target area.

[0168] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0169] If the hail detection result indicates that hail exists in the target area, obtaining at least one updated real scene image corresponding to the target area, and verifying the accuracy of the hail detection result based on the at least one updated real scene image;

[0170] When the hail detection result passes the accuracy verification, the hail detection result is output.

[0171] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0172] If the hail detection result indicates that hail exists in the target area, obtaining environmental data corresponding to the target area, wherein the environmental data includes at least one of a temperature and air humidity corresponding to the target area;

[0173] Detecting whether the environmental data meets the environmental conditions for hail generation;

[0174] If the environmental data meets the environmental conditions for hail generation, the hail detection result is output.

[0175] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0176] If the hail detection result indicates that hail exists in the target area, measuring the current distance information between the distance measuring component and a preset target object in the target area by a distance measuring component;

[0177] Detecting whether a difference between the current distance information and reference distance information corresponding to the target area is greater than a preset distance threshold, wherein the reference distance information is distance information between the distance measuring component and the preset target object measured by the distance measuring component when there is no hail in the target area;

[0178] If the difference between the current distance information and the reference distance information is greater than the preset distance threshold, the hail detection result is output.

[0179] In one embodiment, when the processor executes the computer program, the following steps are also implemented:

[0180] If the hail detection result indicates that hail exists in the target area, sending hail prompt information to the user terminal, where the hail prompt information is used to prompt that hail exists in the target area;

[0181] If a video viewing request sent by the user terminal is received, hail video data corresponding to the target area is acquired, and the hail video data is sent to the user terminal.

[0182] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0183] Acquire a background image corresponding to the target area, where the background image is generated based on at least one historical real scene image corresponding to the target area that does not include hail;

[0184] Acquire at least one target real scene image corresponding to the target area, and compare the target real scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real scene image relative to the background image;

[0185] A hail detection result of the target area is acquired according to the comparison result, and the hail detection result is used to indicate whether hail exists in the target area or does not exist in the target area.

[0186] In one embodiment, the at least one target real scene image is a plurality of target real scene images, and each of the target real scene images is continuous in time sequence.

[0187] In one embodiment, the background image includes a reference background pixel region, and the computer program, when executed, further implements the following steps:

[0188] For each of the target real scene images, determining a target pixel area corresponding to the reference background pixel area from the target real scene image;

[0189] Subtract the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area to obtain a subtraction result;

[0190] The difference result of each of the target real scene images is determined as the comparison result.

[0191] In one embodiment, when the computer program is executed, the following steps are also implemented:

[0192] For each of the target real scene images, detecting whether the difference result of each pixel point in the target pixel area in the target real scene image is greater than a preset difference threshold;

[0193] Determine, from each pixel point of the target pixel area, a plurality of target pixel points whose difference result is greater than the preset difference threshold, and generate a mask map corresponding to the target real scene image according to the plurality of target pixel points, wherein the mask map includes a plurality of mask areas formed by the plurality of target pixel points;

[0194] Detecting whether the attribute information of the plurality of mask regions satisfies a preset attribute condition, wherein the preset attribute condition is related to an area of ​​each of the mask regions and a number of the plurality of mask regions;

[0195] If the attribute information of multiple mask areas of at least one target real scene image satisfies the preset attribute condition, it is determined that the hail detection result is that hail exists in the target area.

[0196] In one embodiment, when the computer program is executed, the following steps are also implemented:

[0197] If the hail detection result indicates that hail exists in the target area, obtaining at least one updated real scene image corresponding to the target area, and verifying the accuracy of the hail detection result based on the at least one updated real scene image;

[0198] When the hail detection result passes the accuracy verification, the hail detection result is output.

[0199] In one embodiment, when the computer program is executed, the following steps are also implemented:

[0200] If the hail detection result indicates that hail exists in the target area, obtaining environmental data corresponding to the target area, wherein the environmental data includes at least one of a temperature and air humidity corresponding to the target area;

[0201] Detecting whether the environmental data meets the environmental conditions for hail generation;

[0202] If the environmental data meets the environmental conditions for hail generation, the hail detection result is output.

[0203] In one embodiment, when the computer program is executed, the following steps are also implemented:

[0204] If the hail detection result indicates that hail exists in the target area, measuring the current distance information between the distance measuring component and a preset target object in the target area by a distance measuring component;

[0205] Detecting whether a difference between the current distance information and reference distance information corresponding to the target area is greater than a preset distance threshold, wherein the reference distance information is distance information between the distance measuring component and the preset target object measured by the distance measuring component when there is no hail in the target area;

[0206] If the difference between the current distance information and the reference distance information is greater than the preset distance threshold, the hail detection result is output.

[0207] In one embodiment, when the computer program is executed, the following steps are also implemented:

[0208] If the hail detection result indicates that hail exists in the target area, sending hail prompt information to the user terminal, where the hail prompt information is used to prompt that hail exists in the target area;

[0209] If a video viewing request sent by the user terminal is received, hail video data corresponding to the target area is acquired, and the hail video data is sent to the user terminal.

[0210] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0211] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0212] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A hail detection method, It is characterized in that The method comprises: Acquire a background image corresponding to the target area, where the background image is generated based on at least one historical real scene image corresponding to the target area that does not include hail; Acquire at least one target real scene image corresponding to the target area, and compare the target real scene image with the background image to obtain a comparison result, wherein the comparison result includes pixel value change information of the target real scene image relative to the background image; For each of the target real scene images, detecting whether the difference result of each pixel point in the target pixel area in the target real scene image is greater than a preset difference threshold; Determine, from each pixel point of the target pixel area, a plurality of target pixel points whose difference result is greater than the preset difference threshold, and generate a mask map corresponding to the target real scene image according to the plurality of target pixel points, wherein the mask map includes a plurality of mask areas formed by the plurality of target pixel points; Detecting whether the attribute information of the plurality of mask regions satisfies a preset attribute condition, wherein the preset attribute condition is related to an area of ​​each of the mask regions and a number of the plurality of mask regions; If the attribute information of multiple mask areas of at least one target real scene image satisfies the preset attribute condition, determining the hail detection result as hail existing in the target area; Wherein, obtaining the background image corresponding to the target area includes: Contours are extracted from the plurality of historical real scene images, connected domains are searched from the extracted contours, and the area of ​​each connected domain is obtained, the connected domains of small areas are filled, and the connected domains of small areas are regionally expanded so that the connected domains of small areas are connected as a whole to form a background, thereby obtaining a background image.

2. The method according to claim 1, It is characterized in that The at least one target real scene image is a plurality of target real scene images, and each of the target real scene images is continuous in time sequence.

3. The method according to claim 2, It is characterized in that The background image includes a reference background pixel area, and the target real scene image is compared with the background image to obtain a comparison result, including: For each of the target real scene images, determining a target pixel area corresponding to the reference background pixel area from the target real scene image; Subtract the pixel value of each pixel point in the target pixel area from the pixel value of the corresponding pixel point in the reference background pixel area to obtain a subtraction result; The difference result of each of the target real scene images is determined as the comparison result.

4. The method according to claim 1, It is characterized in that The method further comprises: If the hail detection result indicates that hail exists in the target area, obtaining at least one updated real scene image corresponding to the target area, and verifying the accuracy of the hail detection result based on the at least one updated real scene image; When the hail detection result passes the accuracy verification, the hail detection result is output.

5. The method according to claim 1, It is characterized in that The method further comprises: If the hail detection result indicates that hail exists in the target area, obtaining environmental data corresponding to the target area, wherein the environmental data includes at least one of a temperature and air humidity corresponding to the target area; Detecting whether the environmental data meets the environmental conditions for hail generation; If the environmental data meets the environmental conditions for hail generation, the hail detection result is output.

6. The method according to claim 1, It is characterized in that The method further comprises: If the hail detection result indicates that hail exists in the target area, measuring the current distance information between the distance measuring component and a preset target object in the target area by a distance measuring component; Detecting whether a difference between the current distance information and reference distance information corresponding to the target area is greater than a preset distance threshold, wherein the reference distance information is distance information between the distance measuring component and the preset target object measured by the distance measuring component when no hail exists in the target area; If the difference between the current distance information and the reference distance information is greater than the preset distance threshold, the hail detection result is output.

7. The method according to claim 1, It is characterized in that The method further comprises: If the hail detection result indicates that hail exists in the target area, sending hail prompt information to the user terminal, where the hail prompt information is used to prompt that hail exists in the target area; If a video viewing request sent by the user terminal is received, hail video data corresponding to the target area is acquired, and the hail video data is sent to the user terminal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program. It is characterized in that When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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