Image recognition method and device

Abstract

The present disclosure provides an image recognition method and device, belonging to the field of image recognition. The method comprises: obtaining first feature information and first target information in a first image; obtaining second target information based on the first feature information and the first target information, wherein the first feature information is information about the feature in the first image, the first target information is information about the target in the first image, and the second target information is actual information about the target; and updating a GIS electronic map based on the second target information. The image recognition method and device provided by the present disclosure can improve the accuracy and reliability of image recognition.

Description

Technical Field

[0001] The present disclosure belongs to the field of image recognition, and more specifically, to an image recognition method and device. Background Art

[0002] With the continuous growth of port operations, port cargo throughput has also increased exponentially. Frequent storage and transportation of cargo necessitates timely updating of cargo distribution information. Currently, GIS electronic maps can quickly identify cargo storage and vacant land. However, GIS electronic map updates are not accurate enough, and cargo size and outlines can easily deviate, leading to misjudgments.

[0003] It can be seen that an accurate image recognition method is urgently needed. Summary of the Invention

[0004] The purpose of the present disclosure is to provide an image recognition method and device to improve the accuracy and reliability of image recognition.

[0005] According to a first aspect of the present disclosure, an image recognition method is provided, comprising:

[0006] Acquire first feature information and first target information in the first image;

[0007] Obtaining target second information based on the first feature information and the first target information, wherein the first feature information is information about the feature in the first image, the first target information is information about the target in the first image, and the second target information is actual information about the target;

[0008] The GIS electronic map is updated based on the target second information.

[0009] According to a second aspect of the present disclosure, an image recognition device is provided, comprising:

[0010] An information acquisition module, configured to acquire first feature information and first target information in the first image;

[0011] an image processing module, configured to obtain second target information based on the first feature information and the first target information, wherein the first feature information is information about the feature in the first image, the first target information is information about the target in the first image, and the second target information is actual information about the target;

[0012] An image updating module is used to update the GIS electronic map based on the target second information.

[0013] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the above-mentioned image recognition method when executing the computer program.

[0014] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned image recognition method are implemented.

[0015] The image recognition method and device provided by the embodiments of the present disclosure have the following beneficial effects:

[0016] This disclosure uses features of known size and position as reference points to accurately calculate the actual size and position of a target. This scale-based calculation method effectively reduces measurement errors caused by factors such as varying shooting heights and ground elevations, thereby improving data accuracy. By acquiring image and target information, this disclosure enables timely updating of GIS electronic maps, ensuring the timeliness and accuracy of GIS electronic map information. This helps warehousing departments save human resources, better plan cargo storage locations, and enhance the accuracy and reliability of image recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A flowchart of an image recognition method provided by an embodiment of the present disclosure;

[0019] Figure 2 A structural block diagram of an image recognition device provided in one embodiment of the present disclosure;

[0020] Figure 3 A schematic block diagram of an electronic device provided in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present disclosure with unnecessary detail.

[0022] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below with reference to the accompanying drawings.

[0023] Please refer to Figure 1 , Figure 1 A flowchart of an image recognition method provided in one embodiment of the present disclosure is provided, the method comprising:

[0024] S101: Acquire first feature information and first target information in a first image.

[0025] In this embodiment, the first image can be acquired through a drone or surveillance. The first image is an image within a target area, and the target area can be a port warehouse.

[0026] The feature can be pre-defined. For example, the feature can be red, 2 meters long, 1 meter wide (no height limit), and have coordinates (200, 300) in the first coordinate system. The first coordinate system can be based on any point within the target area. The positive direction of the X-axis can be due east, and the positive direction of the Y-axis can be due north. It is important to note that when selecting and setting the feature, the principle is that it must not be obscured by the cargo stacks and must be fixed. The feature can be an object inherent to the port warehouse, such as a fixed manhole cover, or it can be artificially placed, such as a square flag with a length of 2 meters and a width of 1 meter.

[0027] The first information of the feature body and the first information of the target in the first image can be obtained through the neural network model and image segmentation trained by the first data set and the second data set. The first data set is a data set formed by images of multiple feature bodies, and the second data set is a data set formed by images of multiple cargo stacks or containers and other cargo stored in port warehouses.

[0028] The first information of the feature may be the length of the feature in the first image, the width of the feature in the first image, and the color of the feature in the first image.

[0029] The target may be a cargo stack or container in a port warehouse. The first target information may be the length of the cargo stack in the first image, the width of the cargo stack in the first image, the color of the target in the first image, and the distance between the target and the feature in the first image.

[0030] For example, the first image is acquired by a drone, and the feature body and the target in the first image can be identified based on a neural network model that has been trained with a large number of feature body images and a large number of target images to obtain first feature body information and second target information. Specifically, it is identified that the color of the feature body P is blue, and the diameter of the feature body P in the first image is 0.03m. Based on the blue color of the feature body, the coordinates of the center point of the blue feature body (feature body P) are retrieved from the pre-stored information to obtain (500, 600). It is identified that the length of the cargo stack Q in the first image is 0.2m, and the width is 0.1m. The color of the cargo stack Q is black and white. The distance between the cargo stack Q and the feature body P in the first image is that the cargo stack Q is 0.05m south of the feature body P.

[0031] S102: Obtaining target second information based on the feature first information and the target first information, wherein the feature first information is information about the feature in the first image, the target first information is information about the target in the first image, and the target second information is actual information about the target.

[0032] In this embodiment, the first information of a feature can be the length, width, and color of the feature in the first image. The first target information can be the length, width, and color of the stack in the first image, and the distance between the stack and the feature. The second target information can be the actual length, width, color, and location of the stack.

[0033] The length of the feature body in the first image may be compared with the actual length of the feature body to obtain a first ratio;

[0034] determining an actual length of the object based on the first ratio and a length of the object in the first image;

[0035] determining an actual width of the object based on the first ratio and a width of the object in the first image;

[0036] An actual distance between the object and the feature is determined based on the first ratio and the distance between the object and the feature in the first image.

[0037] Before comparing the length of the feature body in the first image with the actual length of the feature body to obtain the first ratio, the method further includes:

[0038] Establish a coordinate system based on a point in the target area;

[0039] Determine the coordinates of the feature body;

[0040] The coordinates of the feature body in the first image are obtained based on matching the color of the feature body in the first image with the preset feature body color.

[0041] Through the above process, we can obtain the coordinates of the feature body, the actual length and width of the target, and the actual distance between the target (pallet) and the feature body. The actual color of the target is consistent with the color of the target in the first image.

[0042] For example, if the target area is a port warehouse, the first coordinate system can be established based on the port warehouse gate as the origin, with the positive direction of the X axis being due east and the positive direction of the Y axis being due north.

[0043] The feature's length in the first image is 0.01m, while the actual feature's length is 5m. Therefore, the first scale is 1:500. The target's length in the first image is 0.1m, so the actual target's length is 50m. The target's width in the first image is 0.05m, so the actual target's length is 25m. The target's center in the first image is 0.03m due south of the feature's center, so the actual target's center is 15m due south of the feature's center. The feature's color is yellow. The coordinates of the yellow feature are (500, 700) in the pre-set information. Therefore, the coordinates of the target are (500, 675). The target's color in the first image corresponds to its actual color.

[0044] S103: Update the GIS electronic map based on the target second information.

[0045] In this embodiment, the target may be a container, and the second target information may be the actual length, actual width, actual color, and actual location of the container. A GIS electronic map is an electronic map used by a geographic information system in specific application scenarios, and reflects image information within the target area.

[0046] Determine the second ratio between the target area and the GIS electronic map. The target area may be a port warehouse.

[0047] The position and size of the object on the GIS are determined based on the second scale, the actual length of the object, the actual width of the object, the color of the object, and the actual position of the object. The object may be a container.

[0048] The actual position of the target is displayed in coordinate form. The actual position of the target can be determined based on the distance between the target and the feature in the first image and the coordinates of the feature. The coordinates of the feature are known, and the distance between the target and the feature can be determined based on image segmentation and feature extraction. The coordinates of the feature can be the coordinates of the feature's center, and the distance between the target and the feature can be the distance between the target's center and the feature's center. This distance not only represents a number but also includes a direction. When creating a GIS electronic map, the points in the GIS electronic map should be mapped one-to-one to the coordinate positions.

[0049] For example, the second scale is 200:1, that is, 1m in the GIS electronic image is equal to the actual distance of 200m, the actual length of container A is 6m, the actual width is 2.5m, and the actual position is the coordinate (1, 1). Then, the length of container A displayed in the electronic image is 0.03m, the width is 0.0125m, and the center point of container A is at the coordinate (1, 1).

[0050] As can be seen from the above, the present disclosure can accurately calculate the actual size and position of a target by using a feature of known size and position as a reference. The scale-based calculation method effectively reduces measurement errors caused by factors such as different shooting heights and different ground levels, thereby improving data accuracy. By acquiring images and target information, the present disclosure can promptly update GIS electronic maps, ensuring the timeliness and accuracy of GIS electronic map information, helping warehousing departments save human resources, better plan cargo storage locations, and improve the accuracy and reliability of image recognition.

[0051] In one embodiment of the present disclosure, before acquiring the feature body information and the target first information in the first image, the method further includes:

[0052] Establishing a first coordinate system based on a first origin, wherein the first origin is a point in the target area;

[0053] Determine second information of the feature body, wherein the second information of the feature body is actual information of the feature body.

[0054] In one embodiment of the present disclosure, the second information of the feature body includes: coordinates of the center point of the feature body in the first coordinate system, the color of the feature body, and second contour information of the feature body.

[0055] In one embodiment of the present disclosure, obtaining target second information based on feature first information and target first information includes:

[0056] Determine the coordinates of the center point of the feature body based on the color of the feature body;

[0057] Acquire first contour information of the feature body and first contour information of the target based on image segmentation;

[0058] Obtaining target second contour information based on the feature body first contour information and the target first contour information;

[0059] The target position information is determined based on the coordinates of the center point of the feature body and a first distance between the center point of the feature body and the target.

[0060] In this embodiment, the first origin is any point in the target area, which may be a port warehouse. For example, the first origin is the farthest point in the southwest direction of the port warehouse. Based on this point, a two-dimensional coordinate system is established with the east direction as the positive direction of the X-axis and the north direction as the positive direction of the Y-axis, which is the first coordinate system.

[0061] The second feature information is the actual information of the feature. The actual feature information includes the coordinates of the feature center point in the first coordinate system, the feature color, and the second feature outline information. The feature outline information includes the feature length, feature width, and / or feature diameter. The feature can be rectangular or circular.

[0062] The first device may be a drone or a surveillance device. When strong winds or rain or snow occur and the drone has difficulty completing its flight mission, the first image may be acquired based on the surveillance device.

[0063] The first information of the feature may be the length of the feature in the first image, the width of the feature in the first image, and the color of the feature in the first image.

[0064] When pre-setting the feature body, each feature body in the warehouse can be set to a different color. Based on the color of the feature body in the image, it can be determined which pre-set feature body the feature body in the image is, and the actual length, actual width and actual coordinate position of the feature body can be obtained through the pre-stored feature body information. The above-mentioned actual coordinate position can be the coordinate of the center point of the feature body in the first coordinate system.

[0065] In this embodiment, first contour information of a feature and first contour information of an object can be extracted based on image segmentation. A first ratio can be determined based on the first contour information of the feature and pre-set second contour information of the feature. The first contour information of the feature can be the length, width, and / or diameter of the feature in the first image. The first ratio can be a length ratio, a width ratio, or an area ratio. It should be noted that the area ratio is the square of the length ratio or the width ratio.

[0066] The second contour information of the target can be obtained based on the first ratio and the first contour information of the target. The first contour information of the target can be the length of the target, the width of the target and / or the diameter of the target in the first image. The second contour information of the target can be the length of the target, the width of the target and / or the diameter of the target.

[0067] The coordinate position of the target center point can be determined based on the coordinates of the feature center point and the distance between the feature and the target. The color of the target in the first image is the actual color of the target.

[0068] For example, a first image is obtained by a drone, and based on image segmentation, the length of feature body B in the first image is 0.02m, the width is 0.01m, and the color is green. The distance between the center point of the target and the center point of the feature body in the first image is 0.02m in the east direction. Through pre-stored information, the actual length of the green feature body is 2m, the actual width is 1m, and the coordinates of the center point in the first coordinate system are (100, 200), that is, the first ratio is the length ratio, and the value is 1:100, that is, 1m in the first image is equivalent to an actual distance of 100m.

[0069] Based on image segmentation, the length of target C in the first image is 0.1m and the width is 0.04m. Based on the first ratio and the distance between the target center and the feature center in the first image, which is 0.02m due east, the coordinates of the target center are calculated to be (102, 200). That is, the coordinates of the target center in the first coordinate system are (102, 200), the actual length is 10m, and the actual width is 4m. Since target C is red in the first image, the actual color of target C is also red.

[0070] As can be seen from the above, the present disclosure establishes a first coordinate system based on a first origin in the target area, providing a unified reference framework for locating features and targets. This helps reduce positioning errors caused by different reference points or reference systems, thereby enhancing positioning accuracy. By determining the actual information of the feature (i.e., the feature's secondary information), including its center point coordinates, color, and outline information, the present disclosure provides a rich data foundation for subsequent image analysis and processing, facilitating feature identification and further inferring and calculating target-related information, thereby improving the accuracy and reliability of the image recognition method.

[0071] In one embodiment of the present disclosure, obtaining second contour information of a target based on first feature information and first target information includes:

[0072] Acquire first contour information of the feature body and first contour information of the target based on image segmentation;

[0073] Obtaining a first ratio based on the first contour information of the feature body and the second contour information of the feature body;

[0074] The second contour information of the target is obtained based on the first ratio and the first contour information of the target.

[0075] In one embodiment of the present disclosure, determining target position information based on the coordinates of the center point of the feature body and a first distance between the center point of the feature body and the target includes:

[0076] determining a second distance between the center point of the feature body and the target based on the first distance between the center point of the feature body and the target and the first ratio;

[0077] The target position information is determined based on the coordinates of the feature body center point and the second distance.

[0078] In this embodiment, image segmentation is a basic process in digital image processing. It can decompose an image into several non-overlapping, meaningful regions with the same properties. The purpose of image segmentation is to separate the target in the image from the background to facilitate subsequent analysis and processing.

[0079] The first contour information of the feature body may be the length, width and / or diameter of the feature body in the first image; the first contour information of the object may be the length, width and / or diameter of the object in the first image.

[0080] The second contour information of the feature body may be the actual length, actual width and / or actual diameter of the feature body; the second contour information of the target may be the actual length, actual width and / or actual diameter of the target.

[0081] The first contour information of the feature body and the first contour information of the target can be obtained based on image segmentation and feature extraction, the second contour information of the feature body is pre-set, and the second contour information of the target is calculated based on the first contour information of the feature body, the second contour information of the feature body and the first contour information of the target.

[0082] The first distance between the feature center and the target is the distance between the feature center and the target center in the first image; the second distance between the feature center and the target is the actual distance between the feature center and the target center. The first distance between the feature center and the target center can be obtained based on image segmentation and feature extraction. This distance not only includes a numerical value but also a direction.

[0083] The second distance can be obtained based on the first ratio and the first distance, that is, the actual distance between the center point of the feature body and the center point of the target is obtained based on the first ratio and the distance between the center point of the feature body and the center point of the target in the first image, thereby obtaining the coordinates of the target in the first coordinate system. The first coordinate system is a coordinate system established with any point in the target area as the origin. The positive direction of the X-axis of the first coordinate system can be the due east direction, and the positive direction of the Y-axis can be the due north direction.

[0084] For example, the first ratio can be a length ratio, a width ratio or an area ratio. Specifically, the length of the feature body D in the first image is 0.03m, and the width is 0.02m, that is, the area is 0.0006 square meters. The actual length of the feature body D is 3m, and the width is 2m, that is, the area is 6 square meters. When the first ratio is the length ratio or the width ratio, the first ratio is 1:100. When the first ratio is the area ratio, the first ratio is 1:10000.

[0085] Based on image segmentation and feature extraction, the first contour information of feature E is: its length in the first image is 0.04m, and its width is 0.03m. Based on feature extraction, the color of feature D in the first image is black. Based on pre-set information, the second contour information of feature E is: its actual length is 4m, its width is 3m, and the coordinates of the center point of feature E in the first coordinate system are (1000, 1120). The first ratio is the area ratio, with a value of 1:10000.

[0086] The target first contour information of the stack F is: the length of the stack F in the first image is 0.16m, and the width of the stack F is 0.12m. At this time, since the area ratio is the square of the length ratio or the width ratio, the length ratio is 1:100, so the target second contour information of the stack F is: the actual length of the stack F is 16m, and the actual width is 12m.

[0087] Based on image segmentation and feature extraction, the distance between the center point of the feature body E and the center point of the stack F in the first image is 0.1 m (first distance) due north of the feature body E. Calculated through the first ratio and the first distance, the actual distance between the center point of the stack F and the center point of the feature body E is 10 m, so the coordinates of the center point of the stack F are (1000, 1130).

[0088] As can be seen from the above, the present disclosure uses image segmentation technology to obtain first contour information of features and targets, enabling more accurate identification of the boundaries between target objects and features, thereby reducing errors caused by blurred boundaries or noise interference. The present disclosure compares the second contour information (i.e., actual size) of the feature with the first contour information (i.e., size in the image) to calculate a first ratio, reflecting the relationship between pixel size and actual size in the image. This allows the size information in the image to be converted into actual size, achieving standardization of contour information and improving the reliability and accuracy of image recognition.

[0089] In one embodiment of the present disclosure, updating the GIS electronic map based on the target second information includes:

[0090] determining a second ratio;

[0091] The GIS electronic map is updated based on the second scale and the target second information.

[0092] In one embodiment of the present disclosure, the image recognition method further includes:

[0093] Determine the importance of each coordinate point in the first coordinate system;

[0094] The update frequency is determined based on the importance of each coordinate point.

[0095] In this embodiment, the second ratio is the mapping ratio between the target area and the GIS electronic map, i.e., the second ratio. The second ratio is pre-set and can be manually set based on actual conditions and needs. Before creating a GIS electronic map, the first coordinate system should be determined and the coordinate points in the first coordinate system should be mapped one-to-one with the points in the GIS electronic map.

[0096] The importance of each area in the target area can be defined through historical records or manually. The target area can be a port warehouse, and the update frequency of GIS can be determined according to the importance of each area.

[0097] For example, the origin is determined based on the midpoint of the north-south direction and the midpoint of the east-west direction in the port warehouse, and a coordinate system is established with the origin. The positive direction of the X-axis can be due east, and the positive direction of the Y-axis can be due north. In historical records, if the goods in the area consisting of coordinates (1000, 1000), (1100, 1000), (1000, 1200), and (1100, 1200) have not been updated for a long time, then the coordinates within the X-axis coordinate range of 1000-1100 and the Y-axis coordinate range of 1000-1200 can be defined as having an importance of 1.

[0098] In the area with coordinates (500, 700), (650, 700), (500, 800), and (650, 800), the goods are updated more frequently. Therefore, the importance of coordinates within the X-axis coordinate range of 500-650 and the Y-axis coordinate range of 700-800 can be defined as 2.

[0099] Different importance levels correspond to different update frequencies. For example, an area with an importance level of 1 has an update frequency of 24 hours; an area with an importance level of 2 has an update frequency of 6 hours; and an area with an importance level of 1 has an update frequency of 2 hours. The update frequency corresponding to different importance levels can be manually set.

[0100] The first image is acquired by the UAV, and the position information of the center point of the feature body is identified and determined according to the pre-set information. The position information of the target is obtained according to the image segmentation and feature extraction. The importance of the area where the position information is located is judged, and the update frequency of the area is determined according to the importance.

[0101] From the above, it can be concluded that by determining the second ratio and updating the GIS electronic map based on this ratio, the present disclosure can ensure that the GIS electronic map is consistent with the actual situation, helping to reduce decision-making errors caused by information lag, improving the accuracy and practicality of the GIS electronic map, and improving the accuracy and reliability of image recognition. The present disclosure can more reasonably allocate and manage resources by determining the importance of each coordinate point in the first coordinate system and setting different update frequencies based on the importance. For areas with higher importance, increasing the update frequency can ensure timely acquisition of the latest information; for areas with lower importance, the update frequency can be appropriately reduced to reduce unnecessary resource waste.

[0102] Corresponding to the image recognition method of the above embodiment, Figure 2 This is a structural block diagram of an image recognition device provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The image recognition device 20 includes: an information acquisition module 21, an image processing module 22 and an image updating module 23.

[0103] The information acquisition module 21 is configured to acquire first feature body information and first target information in the first image;

[0104] An image processing module 22 is configured to obtain second target information based on the first feature information and the first target information, wherein the first feature information is information about the feature in the first image, the first target information is information about the target in the first image, and the second target information is actual information about the target;

[0105] The image updating module 23 is used to update the GIS electronic map based on the target second information.

[0106] In one embodiment of the present disclosure, the image recognition device 20 further includes: an image acquisition module;

[0107] an image acquisition module, configured to establish a first coordinate system based on a first origin, wherein the first origin is a point in the target area;

[0108] Determining second information of the feature body, wherein the second information of the feature body is actual information of the feature body;

[0109] A first image is acquired based on a first device.

[0110] In one embodiment of the present disclosure, the image acquisition module is specifically used for obtaining the second information of the feature body, including: coordinates of the center point of the feature body in the first coordinate system, the color of the feature body, and the second contour information of the feature body.

[0111] In one embodiment of the present disclosure, the image processing module 22 is specifically configured to determine the coordinates of the center point of the feature body based on the color of the feature body;

[0112] Acquire first contour information of the feature body and first contour information of the target based on image segmentation;

[0113] Obtaining target second contour information based on the feature body first contour information and the target first contour information;

[0114] The target position information is determined based on the coordinates of the center point of the feature body and a first distance between the center point of the feature body and the target.

[0115] In one embodiment of the present disclosure, the image processing module 22 is further configured to obtain first contour information of the feature body and first contour information of the target based on image segmentation;

[0116] Obtaining a first ratio based on the first contour information of the feature body and the second contour information of the feature body;

[0117] The second contour information of the target is obtained based on the first ratio and the first contour information of the target.

[0118] In one embodiment of the present disclosure, the image processing module 22 is further configured to determine a second distance between the center point of the feature body and the target based on the first distance between the center point of the feature body and the target and the first ratio;

[0119] The target position information is determined based on the coordinates of the feature body center point and the second distance.

[0120] In one embodiment of the present disclosure, the image updating module 23 is specifically configured to determine the second ratio;

[0121] The GIS electronic map is updated based on the second scale and the target second information.

[0122] In one embodiment of the present disclosure, the image recognition device 20 further includes: an update frequency module;

[0123] An update frequency module, used to determine the importance of each coordinate point in the first coordinate system;

[0124] The update frequency is determined based on the importance of each coordinate point.

[0125] See also Figure 3 , Figure 3 This is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0126] It should be understood that in the embodiments of the present disclosure, the processor 301 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0127] The input device 302 may include a touchpad, a fingerprint collection sensor (for collecting user fingerprint information and fingerprint direction information), a microphone, etc. The output device 303 may include a display (LCD, etc.), a speaker, etc.

[0128] The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0129] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of the image recognition method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0130] In another embodiment of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, all or part of the process of the method in the above embodiment is implemented. The computer program can also be used to instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above method embodiments are implemented. The computer program includes computer program code, which can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0131] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the computer-readable storage medium can include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or is about to be output.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or can be an electrical, mechanical or other form of connection.

[0135] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of these units may be selected based on actual needs to achieve the objectives of the embodiments of the present disclosure.

[0136] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0137] The above are only specific embodiments of the present disclosure, but the scope of protection of the present disclosure is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in this disclosure, and such modifications or replacements should be included in the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure should be based on the scope of protection of the claims.

Claims

1. An image recognition method, characterized in that: include: Establishing a first coordinate system based on a first origin, wherein the first origin is a point in the target area; Determine second information of the feature body, wherein the second information of the feature body is actual information of the feature body; the second information of the feature body includes: coordinates of a center point of the feature body in the first coordinate system, color of the feature body, and second contour information of the feature body; Acquire first feature information and first target information in the first image; Obtaining target second information based on the first feature information and the first target information, wherein the first feature information is information about the feature in the first image, the first target information is information about the target in the first image, and the second target information is actual information about the target; Updating the GIS electronic map based on the target second information; The obtaining of target second information based on the feature first information and the target first information includes: Determining the coordinates of the center point of the feature body based on the color of the feature body; Acquire first contour information of the feature body and first contour information of the target based on image segmentation; Acquire the first contour information of the feature body and the first contour information of the target based on image segmentation; Obtaining a first ratio based on the first contour information of the feature body and the second contour information of the feature body; Obtaining second outline information of the target based on the first ratio and the first outline information of the target; Determining target position information based on the coordinates of the center point of the feature body and a first distance between the center point of the feature body and the target; The image recognition method further includes: Determine the importance of each coordinate point in the first coordinate system; The updating frequency of the GIS electronic map of the area corresponding to each coordinate point is determined based on the importance of each coordinate point.

2. The image recognition method according to claim 1, wherein: The determining the target position information based on the coordinates of the center point of the feature body and the first distance between the center point of the feature body and the target includes: determining a second distance between the center point of the feature body and the target based on the first distance between the center point of the feature body and the target and the first ratio; The target position information is determined based on the feature body center point coordinates and the second distance.

3. The image recognition method according to claim 1, wherein: The updating of the GIS electronic map based on the target second information includes: determining a second ratio; The GIS electronic map is updated based on the second scale and the target second information.

4. An image recognition device, characterized in that: include: an image acquisition module, configured to establish a first coordinate system based on a first origin, wherein the first origin is a point in the target area; Determining second information of the feature body, wherein the second information of the feature body is actual information of the feature body; An information acquisition module, configured to acquire first feature information and first target information in the first image; an image processing module, configured to obtain second target information based on the first feature information and the first target information, wherein the first feature information is information about the feature in the first image, the first target information is information about the target in the first image, and the second target information is actual information about the target; An image updating module, configured to update the GIS electronic image based on the target second information; An image processing module, specifically configured to determine the coordinates of a center point of a feature body based on the color of the feature body; Acquire first contour information of the feature body and first contour information of the target based on image segmentation; Acquire the first contour information of the feature body and the first contour information of the target based on image segmentation; Obtaining a first ratio based on the first contour information of the feature body and the second contour information of the feature body; Obtaining second outline information of the target based on the first ratio and the first outline information of the target; Determining target position information based on the coordinates of the center point of the feature body and a first distance between the center point of the feature body and the target; An update frequency module, used to determine the importance of each coordinate point in the first coordinate system; The updating frequency of the GIS electronic map of the area corresponding to each coordinate point is determined based on the importance of each coordinate point.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing a computer program, 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 3 are implemented.

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