An image classification method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose an image classification method and device, electronic equipment and storage medium. The method comprises: obtaining at least two aerial equipment images by using a shooting device to cruise at least one aerial equipment and obtaining at least two aerial equipment images; obtaining cruise route information and cruise time information of the shooting device; and classifying the at least two aerial equipment images according to the at least two aerial equipment images, the cruise route information and the cruise time information, to determine aerial equipment to which the at least two aerial equipment images belong. The technical solution of the embodiments of the present application breaks the work mode of manual classification and filing, realizes automatic identification and classification and filing storage of the shooting images, greatly reduces the risk of manual operation errors, and realizes automatic identification and fine classification of the shooting images by the shooting device through the image content and GPS coordinates and other auxiliary information by using the system artificial intelligence clustering algorithm, and efficiently and accurately automatically files the images to the corresponding directory.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of image processing technology, and in particular to an image classification method, apparatus, electronic device and storage medium. Background Technology

[0002] Drone patrol images are crucial materials and evidence for power grid workers inspecting high-altitude equipment and facilities, forming the foundation for ensuring the safe and stable operation of the distribution network. According to relevant guidelines for safe power operation and the power grid company's management regulations, regular inspections and maintenance of high-altitude equipment are required.

[0003] However, drones have many patrol routes and take a large number of pictures. If the pictures are not classified and managed in a detailed manner, the picture files may become messy and difficult to maintain. This may make it impossible to locate the corresponding high-altitude equipment in a timely manner, which may lead to untimely maintenance of the high-altitude equipment and certain safety hazards.

[0004] Therefore, how to classify drone cruise images is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides an image classification method, apparatus, electronic device, and storage medium to achieve automatic recognition of image content and intelligent classification and archiving of images, significantly reducing the workload of manual image classification and archiving, while improving the accuracy of image archiving, thereby enhancing overall work efficiency.

[0006] In a first aspect, embodiments of the present invention provide an image classification method, including:

[0007] The system uses a camera to cruise at least one high-altitude device and acquire at least two images of the device; wherein the high-altitude device is used to represent a device whose height is greater than a preset height threshold, and the high-altitude device image refers to an image that includes the high-altitude device.

[0008] Obtain the cruise route information and cruise time information of the shooting device;

[0009] Based on the at least two aerial equipment images, the cruise route information, and the cruise time information, the at least two aerial equipment images are classified to determine the aerial equipment to which the at least two aerial equipment images belong.

[0010] Secondly, embodiments of the present invention also provide an image classification device, comprising:

[0011] An image acquisition module is used to cruise at least one high-altitude device using a shooting device and acquire at least two images of the high-altitude device; wherein, the high-altitude device is used to represent a device with an altitude greater than a preset altitude threshold, and the high-altitude device image refers to an image including the high-altitude device;

[0012] The cruise information acquisition module is used to acquire the cruise route information and cruise time information of the shooting device;

[0013] The image classification module is used to classify the at least two aerial equipment images based on the at least two aerial equipment images, the cruise route information, and the cruise time information, so as to determine the aerial equipment to which the at least two aerial equipment images belong.

[0014] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising:

[0015] One or more processors;

[0016] Storage device for storing one or more programs;

[0017] When the one or more programs are executed by the one or more processors, the one or more processors implement the image classification method described in any embodiment of the present invention.

[0018] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the image classification method described in any embodiment of the present invention.

[0019] This invention provides an image classification method, apparatus, electronic device, and storage medium. It involves using a camera to patrol at least one high-altitude device and acquire at least two images of that device. The high-altitude device represents a device with an altitude greater than a preset height threshold, and each high-altitude device image includes the device. The method acquires patrol route information and patrol time information from the camera. Based on the at least two high-altitude device images, the patrol route information, and the patrol time information, the at least two high-altitude device images are classified to determine the high-altitude device to which each image belongs. This invention breaks away from the manual classification and archiving model, enabling automatic identification, classification, and archiving of captured images. It significantly reduces the risk of human error. The system's artificial intelligence clustering algorithm, using image content and auxiliary information such as GPS coordinates, achieves automatic identification and precise classification of images captured by the camera during patrols, efficiently and accurately archiving them to the corresponding directories. Application development is agile, efficient, and flexible; the technology is independently controllable, highly maintainable, and easy to modify and reuse. Attached Figure Description

[0020] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0021] Figure 1 This is a flowchart of an image classification method provided in an embodiment of the present invention;

[0022] Figure 2 This is a flowchart of another image classification method provided in this embodiment of the invention;

[0023] Figure 3 This is a schematic diagram of the structure of an image classification device provided in an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0026] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations (or steps) may be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operation is completed, but may also have additional steps not included in the figures. The process may correspond to a method, function, procedure, subroutine, subroutine, etc.

[0027] The acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0028] Figure 1 This is a flowchart of an image classification method provided in an embodiment of the present invention. This embodiment is applicable to the classification of images of high-altitude equipment. The method of this embodiment can be executed by an image classification device, which can be implemented in hardware and / or software. This device can be configured in an image classification server. The method specifically includes the following steps:

[0029] S110. Use a camera to cruise at least one high-altitude device and acquire at least two images of the high-altitude device.

[0030] The high-altitude equipment is used to characterize devices with a height greater than a preset height threshold, and the high-altitude equipment includes, but is not limited to, poles and towers. In this embodiment of the invention, a camera is used to patrol the poles and towers and acquire images of poles and towers at different heights. The high-altitude equipment image refers to an image that includes the high-altitude equipment; for example, an image that includes the poles and towers.

[0031] As an optional but non-limiting implementation method, the step of using a camera to cruise at least one high-altitude device and acquire at least two images of the high-altitude device includes, but is not limited to, steps A1-A2:

[0032] Step A1: Use a camera to cruise the at least one high-altitude device along a preset cruise route.

[0033] Step A2: Acquire images of different components of the at least one aerial work platform to obtain at least two aerial work platform images, and transmit the at least two aerial work platform images to the local storage via the network.

[0034] The imaging equipment captures images of the aerial equipment from different angles, showing its appearance and details, and transmits these images back to a local storage module via an internal network. This local storage module allows web applications to access the data locally within the user's browser, providing a convenient interface for staff to use.

[0035] S120: Obtain the cruise route information and cruise time information of the shooting device.

[0036] The cruise route information refers to the route taken by the camera when it cruises and captures images of the high-altitude equipment. The cruise route can be pre-set. For example, the cruise route includes high-altitude equipment 1, high-altitude equipment 2, and high-altitude equipment 3. According to the cruise route information, the order in which the camera captures images is high-altitude equipment 1, high-altitude equipment 2, and high-altitude equipment 3.

[0037] Cruise time information can refer to the shooting time required by the camera to shoot at high-altitude equipment and the cruise flight time between adjacent high-altitude equipment. For example, the cruise time can refer to the shooting time required to shoot at high-altitude equipment 1 and the cruise flight time required to cruise from high-altitude equipment 1 to high-altitude equipment 2.

[0038] S130. Based on the at least two aerial equipment images, the cruise route information, and the cruise time information, classify the at least two aerial equipment images to determine the aerial equipment to which the at least two aerial equipment images belong.

[0039] Among them, see Figure 2 After the camera patrols different high-altitude devices and captures a large number of images, the captured images are transmitted to a local server. Staff use artificial intelligence algorithms and modules to intelligently classify and categorize the camera's patrol route and the images of the high-altitude devices.

[0040] As an optional but non-limiting implementation method, the classification of the at least two aerial equipment images based on the at least two aerial equipment images, the cruise route information, and the cruise time information includes, but is not limited to, steps B1-B3:

[0041] Step B1: Determine the cruise sequence of the at least one high-altitude device during the cruise based on the cruise route information.

[0042] Step B2: Determine the shooting time required to photograph at least one high-altitude device and the cruise time required to cruise two adjacent high-altitude devices based on the cruise time information.

[0043] Step B3: Based on the cruise sequence, shooting time, and cruise time, associate the at least two aerial equipment images, cruise route information, and cruise time information, and classify the at least two aerial equipment images.

[0044] The process involves determining the patrol sequence, shooting time, and patrol time between adjacent high-altitude devices based on the patrol route and patrol time information. The at least two high-altitude device images, the patrol route information, and the patrol time information are then associated. For example, the patrol route information determines the patrol sequence as high-altitude device 1, high-altitude device 2, and high-altitude device 3. The patrol time information determines the shooting time required for each high-altitude device and the flight time required for patrolling between adjacent devices, thus determining the time for shooting images of each high-altitude device. Associating the shooting time of each high-altitude device image with the at least two high-altitude device images identifies the high-altitude device in the at least two images. Images of high-altitude devices belonging to the same device are stored in the same file for convenient information processing and on-site maintenance by construction personnel.

[0045] As an optional but non-limiting implementation method, the classification of the at least two aerial equipment images includes, but is not limited to, steps C1-C3:

[0046] Step C1: Obtain the associated aerial equipment image, and uniquely encode the associated aerial equipment image to determine the encoded value of the associated aerial equipment image.

[0047] Step C2: Obtain the preset sampling list of the at least one high-altitude device.

[0048] Step C3: Classify the at least two aerial equipment images according to the preset sampling list of the at least one aerial equipment and the associated aerial equipment image encoding value.

[0049] The process involves: uniquely encoding the associated aerial work platform images to determine their encoding values; acquiring historically collected aerial work platform images and generating a preset sampling list of these images; comparing the encoded values ​​of the associated aerial work platform images with the preset sampling list generated from the historically collected images, and classifying the at least two aerial work platform images. For example, the preset sampling list of aerial work platform images is processed to provide a unique class encoding for the subsequently generated clustered images; for example, aerial power line equipment is encoded as NFDW001-a. An artificial intelligence clustering algorithm is used to cluster the corresponding objects found in the images, i.e., bounding boxes. The bounding box encoding is compared with the unique class encoding provided by the preset sampling list to classify the at least two aerial work platform images.

[0050] As an optional but non-limiting implementation method, the classification of the at least two aerial equipment images based on the preset sampling list of the at least one aerial equipment and the associated aerial equipment image encoding values ​​includes, but is not limited to, steps D1-D3:

[0051] Step D1: If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is greater than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list belong to the same category.

[0052] Step D2: If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is less than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list do not belong to the same category.

[0053] Step D3: If the encoded value of the associated high-altitude equipment image is different from the encoded value provided by the preset sampling list, it is determined that the associated high-altitude equipment image is incorrect and needs to be reshot.

[0054] Based on the content of images captured by the camera and auxiliary information such as GPS coordinates, artificial intelligence technology is used to achieve automatic recognition of image content and intelligent classification and archiving of images through image analysis algorithms and unsupervised clustering models. This significantly reduces the workload of manual image classification and archiving, while improving the accuracy of image archiving, thereby improving overall work efficiency.

[0055] In one optional embodiment of the present invention, the bounding box code is compared with the category-unique code provided by the preset sampling list. If the comparison results are the same, and the similarity between the high-altitude equipment image and the historically acquired high-altitude equipment image is greater than the preset image similarity threshold, then the high-altitude equipment image and the historically acquired high-altitude equipment image belong to the same category.

[0056] In another optional embodiment of the present invention, the bounding box code is compared with the category-unique code provided by the preset sampling list. If the comparison results are the same, and the similarity between the high-altitude equipment image and the historically acquired high-altitude equipment image is less than the preset image similarity threshold, then the high-altitude equipment image and the historically acquired high-altitude equipment image do not belong to the same category.

[0057] In another optional embodiment of the present invention, the bounding box code is compared with the category-unique code provided by the preset sampling list. If the comparison results are different, the aerial equipment image is captured incorrectly and the aerial equipment image data does not match the preset sampling list.

[0058] As an optional but non-limiting implementation method, the step of using a camera to cruise at least one high-altitude device and acquire at least two images of the high-altitude device includes, but is not limited to, steps E1-E2:

[0059] Step E1: If the clarity of at least one of the at least two aerial equipment images is less than a preset clarity threshold, then determine the shooting time information and cruise route information of the at least one aerial equipment image.

[0060] Step E2: Based on the shooting time information and cruise route information of the at least one high-altitude equipment image, reacquire a target high-altitude equipment image with a resolution greater than a preset resolution threshold.

[0061] When comparing aerial equipment images, if the clarity of at least one image is less than a preset clarity threshold, the aerial equipment to which the image needs to be re-captured can be determined based on the aerial equipment image's cruise route information and cruise time information. For example, based on the capture time and cruise time information of the aerial equipment image to be re-captured, the aerial equipment to which the image needs to be re-captured can be determined; for example, if part 2 of aerial equipment 1 needs to be re-captured, the capturing equipment can re-acquire the image of the aerial equipment to be re-captured based on the cruise route information.

[0062] This invention provides an image classification method. It involves using a camera to patrol at least one high-altitude device and acquire at least two images of that device. The high-altitude device represents a device with an altitude greater than a preset height threshold, and each high-altitude device image includes the device. The method acquires the patrol route information and patrol time information of the camera. Based on the at least two high-altitude device images, the patrol route information, and the patrol time information, the at least two high-altitude device images are classified to determine the high-altitude device to which each image belongs. This invention breaks the manual classification and archiving model, enabling automatic identification, classification, and archiving of captured images. It significantly reduces the risk of human error. The system's artificial intelligence clustering algorithm, using image content and auxiliary information such as GPS coordinates, achieves automatic identification and precise classification of images captured by the camera during patrols, efficiently and accurately archiving them to the corresponding directories. The application development is agile, efficient, and flexible, with independent controllability, high maintainability, and easy modification and reuse.

[0063] Figure 3 This is a schematic diagram of the structure of an image classification device provided in an embodiment of the present invention. The device includes: an image acquisition module 310, a cruise information acquisition module 320, and an image classification module 330; wherein,

[0064] The image acquisition module 310 is used to cruise at least one high-altitude device using a shooting device and acquire at least two images of the high-altitude device; wherein, the high-altitude device is used to represent a device with an altitude greater than a preset altitude threshold, and the high-altitude device image refers to an image including the high-altitude device;

[0065] The cruise information acquisition module 320 is used to acquire the cruise route information and cruise time information of the shooting device.

[0066] The image classification module 330 is used to classify the at least two aerial equipment images based on the at least two aerial equipment images, the cruise route information, and the cruise time information, so as to determine the aerial equipment to which the at least two aerial equipment images belong.

[0067] Based on the above embodiments, optionally, the image acquisition module includes:

[0068] The at least one high-altitude device is patrolled using a camera along a preset patrol route;

[0069] Images are acquired from different components of the at least one aerial device to obtain at least two aerial device images, and the at least two aerial device images are transmitted to a local storage device via a network.

[0070] Based on the above embodiments, optionally, the image classification module includes:

[0071] The cruise sequence of the at least one high-altitude device during the cruise is determined based on the cruise route information;

[0072] Based on the cruise time information, determine the shooting time required to photograph the at least one high-altitude device and the cruise time required to cruise two adjacent high-altitude devices;

[0073] Based on the cruise sequence, shooting time, and cruise time, the at least two aerial equipment images, cruise route information, and cruise time information are associated, and the at least two aerial equipment images are classified.

[0074] Based on the above embodiments, optionally, the image classification module further includes:

[0075] Acquire the associated high-altitude equipment image, and uniquely encode the associated high-altitude equipment image to determine the associated high-altitude equipment image encoding value;

[0076] Obtain the preset sampling list of the at least one high-altitude device;

[0077] Based on the preset sampling list of the at least one aerial device and the associated aerial device image encoding value, the at least two aerial device images are classified.

[0078] Based on the above embodiments, optionally, the image classification module further includes:

[0079] If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is greater than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list belong to the same category.

[0080] If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is less than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list do not belong to the same category.

[0081] If the encoded value of the associated high-altitude equipment image is different from the encoded value provided by the preset sampling list, it is determined that the associated high-altitude equipment image is incorrect and needs to be reshot.

[0082] Based on the above embodiments, optionally, the image acquisition module further includes:

[0083] If the clarity of at least one of the at least two aerial equipment images is less than a preset clarity threshold, then the shooting time information and cruise route information of the at least one aerial equipment image are determined.

[0084] Based on the shooting time information and cruise route information of the at least one high-altitude equipment image, a target high-altitude equipment image with a resolution greater than a preset resolution threshold is reacquired.

[0085] The image classification device provided in the embodiments of the present invention can execute the image classification method provided in any of the embodiments of the present invention, and has the corresponding functions and beneficial effects of executing the image classification method. For details, please refer to the relevant operations of the image classification method in the foregoing embodiments.

[0086] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0087] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0088] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0089] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as image classification methods.

[0090] In some embodiments, the image classification method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the image classification method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the image classification method by any other suitable means (e.g., by means of firmware).

[0091] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0092] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0093] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0094] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0095] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0096] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0097] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0098] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. An image classification method, characterized by, The method includes: The system uses a camera to cruise at least one high-altitude device and acquire at least two images of the device; wherein the high-altitude device is used to represent a device whose height is greater than a preset height threshold, and the high-altitude device image refers to an image that includes the high-altitude device. Obtain the cruise route information and cruise time information of the shooting device; Based on the at least two aerial equipment images, cruise route information, and cruise time information, the at least two aerial equipment images are classified to determine the aerial equipment to which the at least two aerial equipment images belong; The step of classifying the at least two high-altitude equipment images based on the at least two high-altitude equipment images, the cruise route information, and the cruise time information includes: The cruise sequence of the at least one high-altitude device during the cruise is determined based on the cruise route information; Based on the cruise time information, determine the shooting time required to photograph the at least one high-altitude device and the cruise time required to cruise two adjacent high-altitude devices; Based on the cruise sequence, shooting time, and cruise time, the at least two high-altitude equipment images, cruise route information, and cruise time information are associated. Acquire the associated high-altitude equipment image, and uniquely encode the associated high-altitude equipment image to determine the associated high-altitude equipment image encoding value; Obtain the preset sampling list of the at least one high-altitude device; If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is greater than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list belong to the same category. If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is less than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list do not belong to the same category. If the encoded value of the associated high-altitude equipment image is different from the encoded value provided by the preset sampling list, it is determined that the associated high-altitude equipment image is incorrect and needs to be reshot.

2. The method of claim 1, wherein, The step of using a camera to cruise at least one high-altitude device and acquire at least two images of the high-altitude device includes: The at least one high-altitude device is patrolled using a camera along a preset patrol route; Images are acquired from different components of the at least one aerial device to obtain at least two aerial device images, and the at least two aerial device images are transmitted to a local storage device via a network.

3. The method of claim 2, wherein, The step of using a camera to cruise at least one high-altitude device and acquire at least two images of the high-altitude device includes: If the clarity of at least one of the at least two aerial equipment images is less than a preset clarity threshold, then the shooting time information and cruise route information of the at least one aerial equipment image are determined. Based on the shooting time information and cruise route information of the at least one high-altitude equipment image, a target high-altitude equipment image with a resolution greater than a preset resolution threshold is reacquired.

4. An image classification apparatus characterized by comprising: The device includes: An image acquisition module is used to cruise at least one high-altitude device using a shooting device and acquire at least two images of the high-altitude device; wherein, the high-altitude device is used to represent a device with an altitude greater than a preset altitude threshold, and the high-altitude device image refers to an image including the high-altitude device; The cruise information acquisition module is used to acquire the cruise route information and cruise time information of the shooting device; The image classification module is used to classify the at least two aerial equipment images based on the at least two aerial equipment images, the cruise route information, and the cruise time information, so as to determine the aerial equipment to which the at least two aerial equipment images belong. Specifically, the image classification module is used for: The cruise sequence of the at least one high-altitude device during the cruise is determined based on the cruise route information; Based on the cruise time information, determine the shooting time required to photograph the at least one high-altitude device and the cruise time required to cruise two adjacent high-altitude devices; Based on the cruise sequence, shooting time, and cruise time, the at least two high-altitude equipment images, cruise route information, and cruise time information are associated. Acquire the associated high-altitude equipment image, and uniquely encode the associated high-altitude equipment image to determine the associated high-altitude equipment image encoding value; Obtain the preset sampling list of the at least one high-altitude device; If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is greater than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list belong to the same category. If the encoded value of the associated high-altitude equipment image is the same as the encoded value provided by the preset sampling list, and the similarity of the high-altitude equipment images is less than the preset similarity threshold, then it is determined that the associated high-altitude equipment image and the high-altitude equipment corresponding to the preset sampling list do not belong to the same category. If the encoded value of the associated high-altitude equipment image is different from the encoded value provided by the preset sampling list, it is determined that the associated high-altitude equipment image is incorrect and needs to be reshot.

5. The apparatus of claim 4, wherein, The image acquisition module includes: The at least one high-altitude device is patrolled using a camera along a preset patrol route; Images are acquired from different components of the at least one aerial device to obtain at least two aerial device images, and the at least two aerial device images are transmitted to a local storage device via a network.

6. An electronic device, comprising: include: One or more processors; Storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the image classification method according to any one of claims 1-3.

7. A storage medium containing computer-executable instructions, wherein: The computer-executable instructions, when executed by a computer processor, are used to perform the image classification method as described in any one of claims 1-3.

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