Unmanned aerial vehicle autonomous inspection system based on front-end real-time defect identification

Abstract

The invention relates to the technical field of unmanned aerial vehicle routing inspection, and discloses an unmanned aerial vehicle autonomous routing inspection system based on front-end real-time defect identification, and the system is characterized in that an edge calculation box is arranged on an unmanned aerial vehicle, so that the edge calculation box participates in routing inspection path planning and navigation, image collection and defect analysis at the front end; therefore, the purpose of autonomous routing inspection can be achieved, the efficiency, continuity and integrity of routing inspection work are effectively guaranteed, and the system is suitable for large-scale popularization and application.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle inspection technology, and in particular to an unmanned aerial vehicle autonomous inspection system based on front-end real-time defect recognition. Background Art

[0002] As a modern and innovative inspection method for power distribution lines, drone inspection technology has shown many significant advantages, including its ability to respond quickly, efficient work efficiency, breakthrough of geographical restrictions, excellent inspection quality and high safety. By introducing drones for defect identification operations, not only the efficiency and quality of inspection work have been greatly improved, but also the physical burden of inspection personnel has been greatly reduced, the operation and maintenance capabilities of power equipment have been enhanced, and the safe and stable operation of the power grid has been ensured.

[0003] Currently, the control operation of drones mainly relies on remote controllers equipped with computing power. The remote controller sends instructions to achieve precise control of the drone to perform inspection tasks and collect high-definition photos of the towers so that they can be immediately transmitted back to the remote controller for subsequent defect analysis and processing. However, this model also has limitations that cannot be ignored. Specifically, since the drone itself does not have the function of autonomous defect recognition, once an abnormality occurs in the connection between the drone and the remote controller, defects cannot be identified in real time. This makes it difficult for humans to accurately judge whether defects exist when the photos taken by the drone are not clear enough or the shooting angle is not good, and it may be necessary to repeat the inspection operation to verify the defects, which undoubtedly increases the workload and cost.

[0004] In addition, in the face of adverse flight conditions, such as when the drone RTK (real-time dynamic differential positioning) connection is unstable and the RTK signal cannot be received, the drone may hover in the air and be unable to continue the scheduled inspection task. This situation not only affects the continuity and integrity of the inspection work, but also poses a potential threat to the safety of the drone.

[0005] In view of this, it is particularly urgent and important to improve and upgrade the existing drone inspection technology.

[0006] The above information is presented as background information only to assist with understanding the present disclosure and no determination or admission is made as to whether any of the above may be used as prior art with respect to the present disclosure. Summary of the invention

[0007] The present invention provides an unmanned aerial vehicle autonomous inspection system based on front-end real-time defect recognition to solve the problems existing in the prior art.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A drone autonomous inspection system based on front-end real-time defect recognition, the method comprising a drone and a control center; wherein:

[0010] The drone includes a fuselage and an edge computing box, a wireless communication module, an image acquisition module and a navigation module arranged on the fuselage;

[0011] The wireless communication module, image acquisition module and navigation module are respectively connected to the edge computing box;

[0012] The navigation module is used to realize the positioning of the drone, and cooperate with the built-in path planning algorithm of the edge computing box to perform inspection path planning and navigation, so as to realize autonomous inspection of the drone;

[0013] The image acquisition module is used to collect images of the tower to be inspected during the inspection process and send them to the edge computing box;

[0014] The edge computing box is used to perform defect analysis on the collected images in real time at the front end, and report to the control center through the wireless communication module when defects are found in the tower to be inspected.

[0015] Furthermore, in the autonomous inspection system for unmanned aerial vehicles based on front-end real-time defect recognition, the navigation module is also used to navigate the unmanned aerial vehicle to the vicinity of the coordinate point of the tower to be inspected during the navigation process;

[0016] The image acquisition module is specifically used to cooperate with the image acquisition algorithm built into the edge computing box to perform multi-angle image acquisition on the tower to be inspected, automatically adjust image acquisition parameters according to the on-site environment during the image acquisition process, and send the acquired images to the edge computing box.

[0017] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, the image acquisition parameters include centering, zoom ratio, focus point and exposure.

[0018] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, the angles for capturing images of the tower to be inspected include the large side, directly above, and the small side.

[0019] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, a trained defect recognition model is deployed in the edge computing box.

[0020] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, the control center is used to formulate a maintenance plan based on the reported defects and send it to the operation and maintenance personnel.

[0021] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, the edge computing box is specifically used for:

[0022] Perform defect analysis on the collected images in real time at the front end;

[0023] When defects are found in the towers to be inspected, they are classified and stored according to the types of defects;

[0024] After the inspection task is completed and the vehicle lands and stops steadily, the wireless communication module is used to report to the control center.

[0025] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, the edge computing box is specifically used to classify and store defects according to the type of defects when defects are found in the tower to be inspected, and record the tower number, inspection number and inspection time during storage.

[0026] Furthermore, in the drone autonomous inspection system based on front-end real-time defect recognition, the edge computing box is also used to control the image acquisition module to collect more images of the tower with identified defects when the tower to be inspected is found to have defects.

[0027] Furthermore, the UAV autonomous inspection system based on front-end real-time defect recognition also includes an operation control tablet;

[0028] The operation control tablet is connected to the drone and is used to control the drone.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] The present invention provides a UAV autonomous inspection system based on front-end real-time defect recognition. By setting an edge computing box on the UAV, the edge computing box at the front end participates in the planning and navigation of the inspection path, image collection and defect analysis, thereby not only achieving the purpose of autonomous inspection, but also effectively ensuring the efficiency, continuity and integrity of the inspection work, and is suitable for large-scale promotion and application.

[0031] The present invention has other features and advantages, which will be apparent from or will be described in detail in the accompanying drawings and the following detailed description incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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 paying creative work.

[0033] Figure 1 This is one of the schematic structural diagrams of a drone autonomous inspection system based on front-end real-time defect recognition provided by an embodiment of the present invention;

[0034] Figure 2 is a schematic diagram of the composition structure of the drone provided by an embodiment of the present invention;

[0035] Figure 3 This is the second structural schematic diagram of an unmanned aerial vehicle autonomous inspection system based on front-end real-time defect recognition provided by an embodiment of the present invention.

[0036] Reference numerals:

[0037] Drone 1, control center 2, operation control tablet 3;

[0038] Edge computing box 11, wireless communication module 12, image acquisition module 13, navigation module 14. DETAILED DESCRIPTION

[0039] In order to explain in detail the possible application scenarios, technical principles, specific schemes that can be implemented, and the purposes and effects that can be achieved, the following is a detailed description of the specific embodiments listed in conjunction with the accompanying drawings. The embodiments described herein are only used to more clearly illustrate the technical solutions of the present application, and are therefore only used as examples, and cannot be used to limit the scope of protection of the present application.

[0040] Reference to "embodiment" herein means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The term "embodiment" appearing in various places in the specification does not necessarily refer to the same embodiment, nor does it particularly limit its independence or association with other embodiments. In principle, in the present application, as long as there is no technical contradiction or conflict, the various technical features mentioned in the embodiments can be combined in any way to form a corresponding implementable technical solution.

[0041] Unless otherwise defined, the technical terms used in this document have the same meanings as those generally understood by those skilled in the art to which this application belongs; the use of relevant terms in this document is only for describing specific embodiments and is not intended to limit this application.

[0042] In the description of this application, the term "and / or" is an expression used to describe the logical relationship between objects, indicating that three relationships may exist, for example, A and / or B, which means: A exists, B exists, and A and B exist at the same time. In addition, the character " / " in this article generally indicates that the objects before and after are in an "or" logical relationship.

[0043] In the present application, terms such as “first” and “second” are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship of quantity, priority or sequence between these entities or operations.

[0044] Without further limitations, in this application, the words "include", "comprises", "has" or other similar expressions used in the sentences are intended to cover non-exclusive inclusion. These expressions do not exclude the presence of additional elements in the process, method or product including the elements, so that the process, method or product including a series of elements may include not only those limited elements, but also other elements not explicitly listed, or also include elements inherent to such process, method or product.

[0045] In this application, expressions such as "greater than", "less than", "exceed" and the like are understood to exclude the number itself; expressions such as "above", "below", "within" and the like are understood to include the number itself. In addition, in the description of the embodiments of this application, "multiple" means more than two (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups", "multiple times", etc., unless otherwise clearly and specifically limited.

[0046] In the description of the embodiments of the present application, space-related expressions used, such as "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "vertical", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicate the orientation or position relationship based on the orientation or position relationship shown in the specific embodiments or drawings, and are only for the convenience of describing the specific embodiments of the present application or facilitating the reader's understanding, and do not indicate or imply that the referred device or component must have a specific position, a specific orientation, or be constructed or operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0047] Unless otherwise expressly specified or limited, in the description of the embodiments of the present application, the terms such as "install", "connect", "connect", "fix", "set", etc. used should be understood in a broad sense. For example, the "connection" can be a fixed connection, a detachable connection, or an integrated setting; it can be a mechanical connection, an electrical connection, or a communication connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. For technicians in the technical field to which the present application belongs, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0048] In view of the defects of the above-mentioned prior art, the applicant, based on many years of rich practical experience and professional knowledge in this field, and in conjunction with the application of theory, actively conducts research and innovation in the hope of creating a technology that can solve the defects of the prior art. After continuous research and design, and after repeated trial production and improvement, the present invention with real practical value was finally created.

[0049] Please refer to Figure 1-3 The embodiment of the present invention provides a drone autonomous inspection system based on front-end real-time defect recognition, the method comprising a drone 1 and a control center 2; wherein,

[0050] The drone 1 includes a fuselage and an edge computing box 11, a wireless communication module 12, an image acquisition module 13 and a navigation module 14 arranged on the fuselage;

[0051] The wireless communication module 12, the image acquisition module 13 and the navigation module 14 are respectively connected to the edge computing box 11;

[0052] The navigation module 14 is used to realize the positioning of the drone 1, and cooperate with the built-in path planning algorithm of the edge computing box 11 to perform inspection path planning and navigation, so as to realize the autonomous inspection of the drone 1;

[0053] The image acquisition module 13 is used to collect images of the tower to be inspected during the inspection process and send them to the edge computing box 11;

[0054] The edge computing box 11 is used to perform defect analysis on the collected images in real time at the front end, and report to the control center 2 through the wireless communication module 12 when defects are found in the tower to be inspected.

[0055] The embodiment of the present invention achieves the front-end intelligent processing of inspection path planning, navigation, image acquisition and defect analysis by cleverly integrating the edge computing module 11 on the drone unit 1. This innovative design not only greatly improves the autonomy and flexibility of autonomous inspection by drones, but also lays a solid foundation for the wide application of the system while ensuring efficient, continuous and comprehensive inspection operations, demonstrating extremely high promotion value and practical potential.

[0056] It is understandable that the structure of the drone 1 is not limited to the above-mentioned components. In fact, it also includes key elements such as a power supply module, a variety of sensors, and a drive motor. These components play an indispensable role in the normal operation of the drone. For example, the power supply module is responsible for providing a continuous and stable energy supply to the drone, the sensor is used to monitor the flight status and environmental parameters of the drone, and the drive motor is the power source for driving the drone to perform various flight actions.

[0057] However, given that these structural designs have been widely studied and applied in the current field of drone technology, and they are not the core innovations of the embodiments of the present invention, they are not elaborated and analyzed in depth in this embodiment. The focus of the present invention is to introduce the edge computing module 11 to achieve the ability of the drone to identify defects in real time at the front end, and the resulting significant improvement in inspection efficiency and autonomy. This embodiment hopes to bring new solutions and application prospects to the field of drone inspection through this innovative design.

[0058] In a specific implementation presented in this embodiment, the function of the navigation module 14 has been further expanded and optimized. It is not limited to realizing the precise positioning and path planning of the drone, but also bears the responsibility of accurately guiding the drone 1 to the vicinity of the predetermined pole tower coordinate point to be inspected during the navigation process. The realization of this function ensures that the drone can perform detailed and comprehensive inspection operations at the position closest to the target pole tower, thereby greatly improving the accuracy and efficiency of the inspection.

[0059] At the same time, the operating mechanism of the image acquisition module 13 is also elaborated in detail. It does not simply perform the task of image capture, but forms a close collaborative relationship with the image acquisition algorithm built into the edge computing box 11. Under this collaborative mechanism, the image acquisition module 13 can perform multi-angle and all-round image acquisition of the tower to be inspected. During this process, the module 13 will automatically adjust the image acquisition parameters according to the actual conditions of the on-site environment, such as lighting conditions, distance and other factors, to ensure that the collected images are both clear and comprehensive. Subsequently, these carefully collected images will be transmitted to the edge computing box 11 in real time for further analysis and processing.

[0060] In a specific practice shown in this embodiment, the image acquisition module 13 has its image acquisition parameters defined and optimized in more detail when cooperating with the edge computing box 11 to perform image acquisition tasks. Specifically, these parameters cover image centering, zoom ratio adjustment, focus selection, and exposure control.

[0061] The image centering setting ensures that the captured image can accurately place the pole tower to be inspected in the center of the picture, thereby avoiding the omission of key information due to improper composition. The flexible adjustment of the zoom ratio enables the image acquisition module 13 to appropriately enlarge or reduce the pole tower according to different distances and detail requirements to obtain the clearest and most detailed image data. The precise selection of the focus point further improves the clarity and layering of the image, ensuring that the details of key parts are accurately presented. The reasonable control of the exposure effectively avoids the phenomenon of overexposure or underexposure of the image due to excessive or weak light, ensuring the overall quality and readability of the image.

[0062] In a specific implementation detailed in this embodiment, a more comprehensive and detailed perspective layout is adopted for the image acquisition work of the pole tower to be inspected. Specifically, when the image acquisition module 13 cooperates with the edge computing box 11 to perform the acquisition task, it will take pictures of the pole tower from three different angles: the large side, the top, and the small side.

[0063] Shooting from the large side angle can capture the environmental conditions and connection status of the tower and the adjacent area of ​​the large side, which helps to find possible line connection problems or external damage. Shooting from the top angle can intuitively show the overall structure and layout of the tower, providing comprehensive and intuitive image data for subsequent defect identification and analysis. Shooting from the small side angle focuses on capturing the situation of the adjacent area of ​​the tower and the small side, which also helps to find potential safety hazards.

[0064] By shooting from these three different angles, not only can comprehensive information about the tower and its surrounding environment be obtained, but also the status of the tower can be carefully observed and analyzed from multiple dimensions. This innovative design not only improves the comprehensiveness and accuracy of image acquisition, but also provides a richer and more reliable basis for subsequent defect identification, analysis, and processing decisions.

[0065] In summary, this embodiment not only enhances the practicality and flexibility of the UAV inspection system by carefully planning and arranging the image acquisition angles, but also provides a more solid guarantee for the safe operation in fields such as power inspection.

[0066] In a specific implementation detailed in this embodiment, the core functions of the edge computing box 11 are further strengthened and expanded. Specifically, this embodiment deploys a set of carefully trained and optimized defect recognition models in the edge computing box 11.

[0067] This defect recognition model is based on a large amount of historical data and practical experience, and is trained through advanced machine learning algorithms. It can quickly and accurately identify and analyze defects in the tower images captured by the image acquisition module 13. Whether it is cracks, corrosion, fractures, or other defects that may affect the safe operation of the tower, this model can accurately identify them and give corresponding judgments and warnings.

[0068] By deploying this defect recognition model in the edge computing box 11, the real-time processing and analysis of the tower image data is realized, which greatly improves the efficiency and accuracy of the inspection work. At the same time, because the model is deployed on the edge, that is, on the drone, the recognition results can be fed back to the control center in real time, thus realizing real-time monitoring and response of the inspection process.

[0069] To sum up, this embodiment not only improves the intelligence level of the drone inspection system by introducing a trained defect recognition model, but also provides more comprehensive and efficient guarantees for safe operation in fields such as power inspection.

[0070] In a specific implementation method described in depth in this embodiment, the roles and responsibilities of the control center 2 are described in more detail. As the central brain of the entire drone autonomous inspection system, the control center 2 not only bears the important task of receiving defect information reported by the drone 1, but also plays a core role in formulating maintenance plans and guiding the actions of operation and maintenance personnel on this basis.

[0071] Specifically, when the drone 1 identifies the defects of the tower through the edge computing box 11 and reports the relevant information to the control center 2 through the wireless communication module 12, the control center 2 will immediately start the corresponding processing process. This process includes but is not limited to the detailed analysis of the defect information, the assessment of the severity, and the formulation of a maintenance plan based on these analysis results.

[0072] The maintenance plan is carefully prepared by the control center 2 based on the specific situation of the defect, combined with the available resources, schedule and urgency of the operation and maintenance personnel. It specifies in detail the key elements such as the specific tasks, execution personnel, required materials, time nodes and safety precautions of the maintenance work, providing clear and actionable guidance for the operation and maintenance personnel.

[0073] After the plan is completed, the control center 2 will send the maintenance plan to the relevant operation and maintenance personnel through appropriate communication channels in a timely manner. After receiving the plan, the operation and maintenance personnel can quickly organize and carry out maintenance work according to the guidance and requirements in it to ensure that defects can be handled in a timely and effective manner.

[0074] In summary, this embodiment, through the careful design and efficient operation of the control center 2, not only achieves rapid response and processing of defect information reported by the drone, but also provides a more solid and comprehensive guarantee for safe operation in the fields of power inspection by formulating maintenance plans and guiding the actions of operation and maintenance personnel. This innovative design undoubtedly further improves the practicality and reliability of the drone autonomous inspection system.

[0075] In one implementation of this embodiment, the edge computing box 11 is specifically used for:

[0076] Perform defect analysis on the collected images in real time at the front end;

[0077] When defects are found in the towers to be inspected, they are classified and stored according to the types of defects;

[0078] After the inspection task is completed and the vehicle lands and stops, the wireless communication module 12 reports to the control center 2 .

[0079] It should be noted that due to the high resolution of defect identification and inspection pictures, directly transmitting these data to the server will consume a lot of time and network resources. Therefore, in this embodiment, a more reasonable strategy is adopted: after the drone completes the inspection task and lands and stops, the defect data and inspection pictures are uploaded to the control center 2 through the wireless communication module 12. This strategy avoids the network bandwidth occupation and flight stability problems that may be caused by transmitting a large amount of data during flight, ensuring that the drone can focus on the inspection task itself.

[0080] In one implementation of this embodiment, the functions of the edge computing box 11 are further refined and optimized. In particular, in terms of classified storage of inspection tower defects, the edge computing box 11 demonstrates a high degree of intelligence and refinement.

[0081] When the image acquisition module 13 captures the tower image and performs real-time analysis through the defect recognition model inside the edge computing box 11, once a defect is found, the edge computing box 11 will immediately start the classification storage mechanism. This mechanism not only requires that the defects be accurately classified according to their types, but also requires that detailed information related to the defects be recorded during storage, including but not limited to the tower number, inspection number, and inspection time.

[0082] The tower number is a unique identifier that ensures that the defect information of each tower can be accurately tracked and recorded. The inspection flight number reflects the number of times the drone inspects the same tower, which helps to evaluate the frequency and effectiveness of the inspection. The inspection time provides the specific time point when the defect was discovered, providing an important time reference for subsequent maintenance plans and operation and maintenance work.

[0083] By storing these key information together with the defect data, the edge computing box 11 not only realizes the comprehensive recording and classification management of defect information, but also provides more abundant and detailed data support for the control center 2. This helps the control center 2 to more accurately assess the severity and impact of the defect, so as to formulate a more reasonable and effective maintenance plan.

[0084] In one implementation of this embodiment, the functions of the edge computing box 11 are further expanded and enhanced. Specifically, when the edge computing box 11 processes image data in real time at the front end, once it detects that a tower to be inspected has defects, it can not only immediately classify and store them, but also intelligently trigger the image acquisition module 13 to perform more in-depth image acquisition on the tower with identified defects.

[0085] This function is designed to obtain more detailed information about defects so that more accurate analysis and judgment can be made later. By increasing the shooting angle, magnification and number of shots of the defective part, this embodiment can capture more key information about the shape, size, location and possible impact of the defect. This information is of great significance for the subsequent maintenance plan formulation, defect handling and preventive measures.

[0086] It is worth noting that the realization of this intelligent trigger mechanism depends on the close coordination and efficient communication between the edge computing box 11 and the image acquisition module 13. The edge computing box 11 can analyze the image data in real time and immediately send instructions to the image acquisition module 13 when defects are found, requiring it to perform more image acquisition. The image acquisition module 13 can quickly adjust the shooting parameters and modes according to the requirements of the instructions to obtain the required image data.

[0087] In one implementation of this embodiment, the entire UAV autonomous inspection system is further improved and upgraded. Specifically, a new component, the operation control tablet 3, is introduced to achieve more comprehensive and flexible control of the UAV 1.

[0088] As the remote control terminal of the UAV 1, the operation control tablet 3 establishes a stable and efficient connection with the UAV 1 through advanced wireless communication technology. This connection enables the operator to monitor and adjust the key functions of the UAV, such as the flight status, inspection tasks and image acquisition, in real time from a safe location far away from the UAV's flight area.

[0089] At the same time, they can also view key parameters such as the drone's flight altitude, speed, and remaining battery power in real time to ensure that the drone can complete its mission safely and efficiently.

[0090] In addition, the Operation Control Tablet 3 also has the function of remotely controlling the drone image acquisition module. The operator can flexibly adjust the acquisition module's shooting parameters, shooting mode, and storage settings through the intuitive interface on the tablet to meet the needs of different inspection scenarios.

[0091] Although the terms such as drone and model are used more frequently in this application, the possibility of using other terms is not excluded. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention.

[0092] An embodiment of the present invention provides a UAV autonomous inspection system based on front-end real-time defect recognition. By setting an edge computing box on the UAV, the edge computing box at the front end participates in the planning and navigation of the inspection path, image collection and defect analysis, thereby not only achieving the purpose of autonomous inspection, but also effectively ensuring the efficiency, continuity and integrity of the inspection work, and is suitable for large-scale promotion and application.

[0093] Finally, it should be noted that although the above embodiments have been described in the specification and drawings of this application, this does not limit the scope of patent protection of this application. All technical solutions generated by replacing or modifying equivalent structures or equivalent processes based on the essential concept of this application using the contents recorded in the specification and drawings of this application, as well as directly or indirectly implementing the technical solutions of the above embodiments in other related technical fields, are included in the scope of patent protection of this application.

Claims

1. An autonomous inspection system for unmanned aerial vehicles based on front-end real-time defect recognition, characterized in that: The method comprises a drone (1) and a control center (2); wherein: The drone (1) comprises a fuselage and an edge computing box (11), a wireless communication module (12), an image acquisition module (13) and a navigation module (14) arranged on the fuselage; The wireless communication module (12), the image acquisition module (13) and the navigation module (14) are respectively connected to the edge computing box (11); The navigation module (14) is used to realize the positioning of the drone (1), and cooperate with the built-in path planning algorithm of the edge computing box (11) to perform inspection path planning and navigation, so as to realize autonomous inspection of the drone (1); The image acquisition module (13) is used to acquire images of the tower to be inspected during the inspection process and send the images to the edge computing box (11); The edge computing box (11) is used to perform defect analysis on the collected images in real time at the front end, and report to the control center (2) via the wireless communication module (12) when defects are found in the pole tower to be inspected.

2. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 1 is characterized in that: The navigation module (14) is also used to navigate the drone (1) to the vicinity of the coordinate point of the tower to be inspected during the navigation process; The image acquisition module (13) is specifically used to cooperate with the image acquisition algorithm built into the edge computing box (11) to perform multi-angle image acquisition on the pole tower to be inspected, automatically adjust image acquisition parameters according to the on-site environment during the image acquisition process, and send the acquired images to the edge computing box (11).

3. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 2 is characterized in that: The image acquisition parameters include centering, zoom ratio, focus point and exposure.

4. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 2 is characterized in that: The angles for collecting images of the tower to be inspected include the large side, directly above, and the small side.

5. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 1 is characterized in that: A trained defect recognition model is deployed in the edge computing box (11).

6. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 1 is characterized in that: The control center (2) is used to formulate a maintenance plan based on the reported defects and send it to the operation and maintenance personnel.

7. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 1 is characterized in that: The edge computing box (11) is specifically used for: Perform defect analysis on the collected images in real time at the front end; When defects are found in the towers to be inspected, they are classified and stored according to the types of defects; After the inspection task is completed and the vehicle lands and comes to a stable stop, the wireless communication module (12) reports to the control center (2).

8. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 7 is characterized in that: The edge computing box (11) is specifically used to classify and store defects according to the type of defects when defects are found in the tower to be inspected, and to record the tower number, inspection number and inspection time during storage.

9. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 1 is characterized in that: The edge computing box (11) is also used to control the image acquisition module (13) to acquire more images of the pole tower with identified defects when a pole tower to be inspected is found to have defects.

10. The UAV autonomous inspection system based on front-end real-time defect recognition according to claim 1 is characterized in that: Also includes an operation control tablet (3); The operation control tablet (3) is connected to the drone (1) and is used to control the drone (1).

Images