Method, device and equipment for correcting deviation of front-end identification auxiliary holder and medium

Abstract

The invention discloses a front-end identification auxiliary holder correction method, device and equipment and a medium. The method comprises the following steps: when an inspection task is executed to reach a new inspection point on a power grid power transmission and distribution line, collecting a power grid power transmission and distribution line image at the new inspection point; determining a rotation angle of an unmanned aerial vehicle holder according to a target inspection object in the power grid power transmission and distribution line image; a camera is fixed on the unmanned aerial vehicle holder; and after the unmanned aerial vehicle holder is adjusted according to the rotation angle, collecting a target inspection object image. The embodiment of the invention can improve the inspection efficiency of the power transmission and distribution line of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid management, and in particular to a front-end identification-assisted pan / tilt deviation correction method, device, equipment and medium. Background Art

[0002] The power transmission and distribution lines of the power grid are very important. Overhead power transmission and distribution lines are widely distributed, requiring a large number of people and a lot of time for manual inspection. In order to better improve work efficiency and ensure personnel safety, drone inspection is proposed. During the drone inspection process, the drone analyzes the route for autonomous inspection. The drone flies to a specific location planned in advance to take pictures and transmit them back to the ground station in real time for artificial intelligence recognition to confirm whether manual intervention maintenance is required.

[0003] In the existing drone inspection process, according to the task points of the route planning, the images taken may have the target object deviating from or overflowing the camera's field of view, resulting in incomplete or unclear points that need to be inspected. As a result, personnel need to spend a lot of time to re-plan routes and operations, and the inspection efficiency is low. In addition, the existing inspection images do not clearly capture small inspection objects such as hanging points and small hardware. As a result, the subsequent intelligent recognition cannot identify the inspection object, which makes it impossible for personnel to locate the problem, resulting in problems such as missed inspections and misidentification, further reducing the inspection efficiency. Summary of the invention

[0004] The present invention provides a front-end identification auxiliary pan / tilt deviation correction method, device, equipment and medium to improve the inspection efficiency of power transmission and distribution lines of a power grid.

[0005] According to one aspect of the present invention, a front-end recognition assisted gimbal deviation correction method is provided, comprising:

[0006] When a new inspection point on the power transmission and distribution line of the power grid is reached during the inspection task, an image of the power transmission and distribution line of the power grid at the new inspection point is collected;

[0007] Determining the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image; the drone gimbal is fixed with a camera;

[0008] After adjusting the drone gimbal according to the rotation angle, an image of the target inspection object is collected.

[0009] According to another aspect of the present invention, a front-end recognition auxiliary gimbal deviation correction device is provided, comprising:

[0010] The original image acquisition module is used to acquire the image of the power transmission and distribution line of the power grid at the new inspection point when the inspection task is performed and the new inspection point is reached on the power transmission and distribution line of the power grid;

[0011] A rotation angle determination module is used to determine the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image; the drone gimbal is fixed with a camera;

[0012] The target image acquisition module is used to acquire the target inspection object image after adjusting the drone gimbal according to the rotation angle.

[0013] According to another aspect of the present invention, a computer program product is provided, including a computer program, which, when executed by a processor, implements the front-end recognition assisted gimbal deviation correction method according to any embodiment of the present invention.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the front-end recognition assisted gimbal correction method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the front-end recognition assisted gimbal correction method described in any embodiment of the present invention when executed.

[0019] The drone of the embodiment of the present invention independently identifies the inspection object and calculates the pan-tilt rotation angle, eliminating the interaction process with the ground station, and directly implements the correction function inside the drone, reducing unnecessary processes and greatly improving the inspection efficiency of the drone; by helping the camera to correct the deviation, the image of the inspection object is more comprehensive and clear, which solves the problem of target object being out of range or unclear in drone photography during previous drone inspections, resulting in the need for ground station personnel to re-adjust the shooting angle and longitude and latitude coordinates of the route file and fly repeatedly to obtain images that meet the requirements, greatly improving work efficiency and personnel usage costs.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a front-end recognition assisted gimbal deviation correction method provided according to an embodiment of the present invention;

[0023] Figure 2 is a flow chart of a front-end recognition assisted gimbal deviation correction method provided according to another embodiment of the present invention;

[0024] Figure 3 is a structural schematic diagram of a front-end recognition assisted gimbal deviation correction method provided according to another embodiment of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Figure 1A flow chart of a front-end identification assisted gimbal deviation correction method provided by an embodiment of the present invention. This embodiment can be applied to the situation where a drone arrives at an inspection point to collect images and returns the images to the ground station according to an inspection task received from a ground station. The method can be executed by a front-end identification assisted gimbal deviation correction device, which can be implemented in the form of hardware and / or software. The device can be configured in an electronic device with corresponding data processing capabilities, such as a drone. The method includes:

[0029] S110. When a new inspection point on a power transmission and distribution line of a power grid is reached during the inspection task, an image of the power transmission and distribution line of the power grid at the new inspection point is collected.

[0030] S120: Determine the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image.

[0031] S130, after adjusting the drone gimbal according to the rotation angle, collect an image of the target inspection object.

[0032] Among them, the camera is fixed on the gimbal of the drone. The inspection objects are the devices, equipment, parts and other objects on the power transmission and distribution lines that need to be inspected.

[0033] Specifically, the drone ground station parses the route file to generate inspection tasks and sends the inspection tasks to the drone. The drone performs the inspection task and inspects the power transmission and distribution lines along the route specified by the inspection task. The route consists of multiple inspection points. Every time the drone arrives at an inspection point, it needs to inspect the inspection point and return the images collected at the inspection point to the ground station.

[0034] When the drone arrives at a new inspection point that has not been inspected before, it uses the camera fixed on the drone gimbal to collect a complete image of the power transmission and distribution line, that is, the power transmission and distribution line image. Through image recognition technology, it determines the various inspection objects in the power transmission and distribution line image, and selects one inspection object as the target inspection object.

[0035] Considering that the target inspection object is usually not exactly in the center of the power grid transmission and distribution line image, it is necessary to determine the rotation angle of the camera aimed at the target inspection object based on the offset of the target inspection object in the power grid transmission and distribution line image from the center point of the power grid transmission and distribution line image.

[0036] The drone controls the gimbal to rotate according to the rotation angle. After the rotation is completed, the camera is aimed at the target inspection object. At this time, the image captured by the camera is the target inspection object image with the inspection object located in the center of the image.

[0037] The drone of the embodiment of the present invention independently identifies the inspection object and calculates the pan-tilt rotation angle, eliminating the interaction process with the ground station, and directly implements the correction function inside the drone, reducing unnecessary processes and greatly improving the inspection efficiency of the drone; by helping the camera to correct the deviation, the image of the inspection object is more comprehensive and clear, which solves the problem of target object being out of range or unclear in drone photography during previous drone inspections, which requires ground station personnel to re-adjust the shooting angle and longitude and latitude coordinates of the route file and fly repeatedly to obtain images that meet the requirements, greatly improving work efficiency and personnel usage costs.

[0038] On the basis of the above embodiment, optionally, after determining the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image, the method further includes:

[0039] The rotation angle, the new inspection point and the target inspection object are recorded in association to obtain a historical inspection record;

[0040] Accordingly, the method further includes:

[0041] When the inspection mission reaches the old inspection point on the power grid transmission and distribution line, the rotation angle of the drone gimbal is determined based on the historical inspection records.

[0042] Specifically, when the drone reaches an old inspection point that has been inspected before, it no longer needs to identify the target inspection object and calculate the rotation angle, but directly obtains the rotation angle and target inspection object associated with the old inspection point from the historical inspection record. Then, the drone controls the drone to rotate according to the rotation angle. After the rotation is completed, the camera is aimed at the target inspection object. At this time, the image captured by the camera is the target inspection object image with the inspection object located in the center of the image.

[0043] Figure 2 This is a flow chart of a front-end recognition assisted gimbal deviation correction method provided by another embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2 As shown, the method includes:

[0044] S210. When a new inspection point on a power transmission and distribution line of a power grid is reached during the inspection task, an image of the power transmission and distribution line of the power grid at the new inspection point is collected.

[0045] S220: Determine at least two candidate inspection objects in the power grid transmission and distribution line image; and determine a target inspection object from the at least two candidate inspection objects.

[0046] S230. Determine the rotation angle of the drone gimbal according to the offset between the target inspection object and the center point of the power grid transmission and distribution line image.

[0047] Specifically, the candidate inspection objects in the power grid transmission and distribution line image are determined by image recognition technology. If there is only one candidate inspection object, the candidate inspection object is directly determined as the target inspection object. If there are multiple candidate inspection objects, a target inspection object is selected from these candidate inspection objects. After the target inspection object is determined, the offset of the target inspection object in the power grid transmission and distribution line image from the center point of the power grid transmission and distribution line image is calculated, and the rotation angle required for the drone gimbal to align with the target inspection object is calculated based on the offset.

[0048] Optionally, determining the target inspection object from the at least two candidate inspection objects includes:

[0049] For each candidate inspection object, determine the confidence, area ratio and distance from the center of the image of the candidate inspection object according to the determined power grid transmission and distribution line image;

[0050] The confidence level, the area percentage, and the distance from the center of the image are respectively multiplied by their corresponding weights and then summed to obtain a score for the candidate inspection object;

[0051] The candidate inspection object with the highest score is determined as the target inspection object.

[0052] The confidence of the candidate inspection object must be greater than or equal to a confidence threshold (eg, 85%), and the inspection object with a confidence lower than the confidence threshold will not be used as a candidate inspection object.

[0053] Specifically, for each candidate inspection object, first determine the confidence, area ratio and distance from the center of the image of the power grid transmission and distribution line of the candidate inspection object. The weight coefficients of these three data are different, for example, the weight coefficient of the area ratio (0.5)> the weight coefficient of the distance from the center of the image (0.3)> the weight coefficient of the confidence (0.2). Multiply the confidence by its weight coefficient to obtain the first sub-score; multiply the area ratio by its weight coefficient to obtain the second sub-score; multiply the distance from the center of the image by its weight coefficient to obtain the third sub-score. The sum of the first, second and third sub-scores is determined as the score of the candidate inspection object. After completing the score calculation of each candidate inspection object, the candidate inspection object with the highest score is determined as the target inspection object.

[0054] Optionally, the rotation angle is determined by the following formula:

[0055]

[0056]

[0057]

[0058] Among them, Fpx is the focal length of the camera in pixels, n' is the diagonal size of the camera sensor, pw and ph are the width and height of the grid transmission and distribution line image, and F mm is the focal length of the camera in millimeters; A x is the rotation angle in the x-axis direction, x px is the x-coordinate of the target inspection object in the power grid transmission and distribution line image in pixel units, cx px A is the x-coordinate of the center point of the power transmission and distribution line image in pixel units; y is the rotation angle along the y-axis, y px is the y coordinate of the target inspection object in the power grid transmission and distribution line image in pixel units, cy px The y coordinate of the center point of the power grid transmission and distribution line image in pixels.

[0059] Specifically, the coordinates of the center point of the target inspection object in the power grid transmission and distribution line image are used as the coordinates of the target inspection object in the power grid transmission and distribution line image. Based on the coordinates of the target inspection object and the coordinates of the center point of the power grid transmission and distribution line image, the offset (x px -cx px ) and the y-direction offset (y px -cy px ), and calculate the rotation angle of the drone gimbal in the X-axis direction and the rotation angle on the Y-axis based on these two.

[0060] S240. After adjusting the UAV gimbal according to the rotation angle, collect a target inspection object image; and send the target inspection object image and the power grid transmission and distribution line image to a UAV ground station in association.

[0061] Specifically, after obtaining the target inspection object image, the drone returns the target inspection object image and the power grid transmission and distribution line image to the drone ground station. The drone ground station can further process the target inspection object image and the power grid transmission and distribution line image, for example, by manually or through anomaly detection models to determine whether the target inspection object in the target inspection object image has an abnormality.

[0062] The embodiment of the present invention improves the calculation accuracy of the rotation angle by converting the camera focal length between pixel units and millimeter units.

[0063] Figure 3 A schematic diagram of the structure of a front-end recognition auxiliary gimbal deviation correction device provided by another embodiment of the present invention. Figure 3 As shown, the device comprises:

[0064] The original image acquisition module 310 is used to acquire the image of the power transmission and distribution line of the power grid at the new inspection point when the inspection task is performed and the new inspection point is reached on the power transmission and distribution line of the power grid;

[0065] A rotation angle determination module 320 is used to determine the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image; the drone gimbal is fixed with a camera;

[0066] The target image acquisition module 330 is used to acquire the target inspection object image after adjusting the drone gimbal according to the rotation angle.

[0067] The front-end recognition assisted gimbal deviation correction device provided in the embodiment of the present invention can execute the front-end recognition assisted gimbal deviation correction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0068] Optionally, the rotation angle determination module 320 includes:

[0069] An object determination unit, configured to determine at least two candidate inspection objects in the power grid transmission and distribution line image;

[0070] An object screening unit, used to determine a target inspection object from the at least two candidate inspection objects;

[0071] A rotation angle determination unit is used to determine the rotation angle of the drone gimbal according to the offset between the target inspection object and the center point of the power grid transmission and distribution line image. It includes:

[0072] Optionally, the object screening unit is specifically used to: for each candidate inspection object, determine the confidence, area percentage and distance from the center of the image of the power grid transmission and distribution line according to the image; multiply the confidence, area percentage and distance from the center of the image by their respective corresponding weights and then sum them up to obtain the score of the candidate inspection object; and determine the candidate inspection object with the highest score as the target inspection object.

[0073] Optionally, the rotation angle is determined by the following formula:

[0074]

[0075]

[0076]

[0077] Among them, F px is the focal length of the camera in pixels, n' is the diagonal size of the camera sensor, pw and ph are the width and height of the grid transmission and distribution line image, and F mmis the focal length of the camera in millimeters; A x is the rotation angle in the x-axis direction, x px is the x-coordinate of the target inspection object in the power grid transmission and distribution line image in pixel units, cx px A is the x-coordinate of the center point of the power transmission and distribution line image in pixel units; y is the rotation angle along the y-axis, y px is the y coordinate of the target inspection object in the power grid transmission and distribution line image in pixel units, cy px The y coordinate of the center point of the power grid transmission and distribution line image in pixels.

[0078] Optionally, the device further comprises:

[0079] Image return module, used to send the target inspection object image and the power grid transmission and distribution line image to the UAV ground station

[0080] Optionally, the device further comprises:

[0081] A record saving module, used to associate and record the rotation angle, the new inspection point and the target inspection object to obtain a historical inspection record;

[0082] The record reading module is used to determine the rotation angle of the drone gimbal according to the historical inspection records when the inspection task reaches the old inspection point on the power grid transmission and distribution line.

[0083] The front-end recognition assisted gimbal deviation correction device further described can also execute the front-end recognition assisted gimbal deviation correction method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0084] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device 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 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, 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 examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0085] like Figure 4As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0086] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0087] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the front-end recognition auxiliary gimbal correction method.

[0088] In some embodiments, the front-end identification auxiliary gimbal deviation correction method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the front-end identification auxiliary gimbal deviation correction method described above can be performed. Alternatively, in other embodiments, the processor 41 can be configured to execute the front-end identification auxiliary gimbal deviation correction method in any other appropriate manner (for example, by means of firmware).

[0089] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0090] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0091] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0092] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0093] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0094] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0095] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0096] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A front-end recognition-assisted gimbal deviation correction method, It is characterized in that Applied to a drone, the method comprises: When a new inspection point on the power transmission and distribution line of the power grid is reached during the inspection task, an image of the power transmission and distribution line of the power grid at the new inspection point is collected; Determining the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image; the drone gimbal is fixed with a camera; After adjusting the drone gimbal according to the rotation angle, an image of the target inspection object is collected.

2. The method according to claim 1, It is characterized in that Determining the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image includes: Determining at least two candidate inspection objects in the power grid transmission and distribution line image; Determine a target inspection object from the at least two candidate inspection objects; The rotation angle of the drone gimbal is determined according to the offset between the target inspection object and the center point of the power grid transmission and distribution line image.

3. The method according to claim 2, It is characterized in that Determining the target inspection object from the at least two candidate inspection objects includes: For each candidate inspection object, determine the confidence, area ratio and distance from the center of the image of the candidate inspection object according to the determined power grid transmission and distribution line image; The confidence level, the area percentage, and the distance from the center of the image are respectively multiplied by their corresponding weights and then summed to obtain a score for the candidate inspection object; The candidate inspection object with the highest score is determined as the target inspection object.

4. The method according to claim 2, It is characterized in that The rotation angle is determined by the following formula: Among them, F px is the focal length of the camera in pixels, n' is the diagonal size of the camera sensor, pw and ph are the width and height of the grid transmission and distribution line image, and F mm is the focal length of the camera in millimeters; A x is the rotation angle in the x-axis direction, x px is the x-coordinate of the target inspection object in the power grid transmission and distribution line image in pixel units, cx px A is the x-coordinate of the center point of the power transmission and distribution line image in pixel units; y is the rotation angle along the y-axis, y px is the y coordinate of the target inspection object in the power grid transmission and distribution line image in pixel units, cy px The y coordinate of the center point of the power grid transmission and distribution line image in pixels.

5. The method according to claim 1, It is characterized in that After acquiring the target inspection object image, the method further includes: The target inspection object image and the power grid transmission and distribution line image are sent to the UAV ground station in association.

6. The method according to claim 1, It is characterized in that After determining the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image, the method further includes: The rotation angle, the new inspection point and the target inspection object are recorded in association to obtain a historical inspection record; Accordingly, the method further includes: When the inspection mission reaches the old inspection point on the power grid transmission and distribution line, the rotation angle of the drone gimbal is determined based on the historical inspection records.

7. A front-end identification auxiliary PTZ deviation correction device, It is characterized in that Deployed on a drone, the device comprises: The original image acquisition module is used to acquire the image of the power transmission and distribution line of the power grid at the new inspection point when the inspection task is performed and the new inspection point is reached on the power transmission and distribution line of the power grid; A rotation angle determination module is used to determine the rotation angle of the drone gimbal according to the target inspection object in the power grid transmission and distribution line image; the drone gimbal is fixed with a camera; The target image acquisition module is used to acquire the target inspection object image after adjusting the drone gimbal according to the rotation angle.

8. The device according to claim 7, It is characterized in that The rotation angle determination module comprises: An object determination unit, configured to determine at least two candidate inspection objects in the power grid transmission and distribution line image; An object screening unit, used to determine a target inspection object from the at least two candidate inspection objects; The rotation angle determination unit is used to determine the rotation angle of the drone gimbal according to the offset between the target inspection object and the center point of the power grid transmission and distribution line image.

9. An electronic device, It is characterized in that The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the front-end recognition assisted gimbal correction method described in any one of claims 1-6.

10. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the front-end recognition assisted gimbal deviation correction method described in any one of claims 1 to 6 when executed.

Images