54 results about "Flight inspection" patented technology

The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an intelligent unmanned aerial vehicle bridge slope crack detection and early warning system, which comprises an unmanned aerial vehicle cruise unit and a detection and early warning management unit, and is characterized in that the unmanned aerial vehicle cruise unit is used for performing autonomous flight inspection on a bridge slope in a target area according to a preset period; the visible light image, the infrared thermal imaging and the three-dimensional laser point cloud data of the target area are synchronously obtained; the detection and early warning management unit is used for processing the image data acquired by the unmanned aerial vehicle cruise unit in real time through a pre-trained crack intelligent analysis algorithm, extracting geometric features and thermodynamic features of the crack, constructing a time sequence model to predict a crack expansion trend and a structure safety risk level, and outputting the crack expansion trend and the structure safety risk level. A multi-level early warning signal is generated based on the risk level and is pushed to the maintenance terminal; the geometrical characteristics comprise crack depth and strike angle, and the thermodynamic characteristics comprise abnormal temperature gradient distribution. The problems of long detection period, high cost and terrain limitation in the prior art are solved.

The invention discloses an unmanned aerial vehicle inspection management method, system and device based on artificial intelligence and a medium. Performing flight inspection by using the unmanned aerial vehicle based on the user task, and obtaining traffic image data on the inspection line; transmitting the acquired traffic image data to a data processing terminal; performing data analysis on the acquired traffic image data based on an artificial intelligence technology to obtain a traffic condition analysis result; and determining a corresponding processing scheme according to the analysis result based on an artificial intelligence technology, and feeding back the processing scheme to the control terminal. According to the invention, the unmanned aerial vehicle is used for carrying out traffic inspection, the obtained image is processed in real time based on the artificial intelligence technology, the current traffic condition is accurately and intelligently analyzed, the characteristic traffic condition is obtained, the corresponding processing scheme is scientifically selected, and the specific processing information is rapidly and automatically generated and fed back to the control personnel. The traffic guidance pressure is greatly reduced, the intelligent process is optimized, and the intelligence of traffic dispersion and the overall working efficiency are improved.

The invention relates to a mapping system and method for flight inspection of indoor equipment, the system comprises a data acquisition end and an offline data processing end, the data acquisition end comprises an unmanned aerial vehicle and a sensing module, and the sensing module comprises a laser radar module, a binocular camera module, an inertial measurement module and an airborne recording module; the method comprises the following steps: synchronously acquiring multi-source data through layered flight; performing space-time alignment and preprocessing; self-adaptive sampling is carried out based on point cloud geometric prior guide image features, and laser-vision joint features are generated; fusing point cloud registration, joint features and inertial data, and carrying out joint estimation on adjacent frame pose increments; performing loopback detection based on the key frame to generate a closed-loop constraint; constructing and optimizing a pose map to eliminate cumulative drift; and finally fusing to generate a global point cloud map. According to the method, the space consistency and geometric accuracy of the map can be effectively improved, and a reliable data basis is provided for digital modeling and intelligent operation and maintenance of indoor equipment inspection.

An unmanned hot-line work control system based on an autonomous decision algorithm performs flight inspection on a power transmission line through an unmanned aerial vehicle according to a predetermined path, and analyzes the state of the power transmission line in real time by using an image processing technology; and then, a multi-stage feature extraction method is adopted, that is, shallow features are extracted from an original image, deep semantic features are further mined, and finally, the information is integrated through a feature joint perception module to generate a final decision result. Besides, a feedback mechanism is further designed, when suspected abnormity is detected, the unmanned aerial vehicle can automatically adjust the position and hover above a target position, so that more detailed high-definition images are collected, and the data are transmitted back to a ground server for further analysis. Therefore, the fault diagnosis speed is improved, the possibility of false alarm is reduced, and the reliability and practicability of the whole system are improved.

The present invention relates to the technical field of flight inspection, and discloses a flight inspection processing method and system for a ground-based navigation system, including: creating a flight inspection task for a ground-based navigation device and configuring an autonomous positioning device for the inspection aircraft; collecting the autonomous positioning information of the inspection aircraft under the autonomous positioning device, and collecting the real-time signal data of the ground-based navigation device in the inspection flight task to detect the signal deviation of the ground-based navigation device in real time; setting up a signal deviation early warning mechanism for the ground-based navigation device, configuring a multi-index monitoring network for the ground-based navigation device, identifying the flight interval of the inspection aircraft, and creating a parameter dynamic calibration system for the ground-based navigation device; combining the autonomous positioning device, the multi-index monitoring network, the signal deviation early warning mechanism and the parameter dynamic calibration system to perform the flight inspection processing of the ground-based navigation device and obtain the flight inspection result. The present invention can improve the accuracy of the performance evaluation of the ground-based navigation system.

The utility model discloses a photovoltaic station intelligent inspection tour device, which comprises an unmanned aerial vehicle, a base is arranged on the outer wall of the lower side of the unmanned aerial vehicle, a fixing mechanism is arranged on the base, the fixing mechanism comprises an abutting fixing rod and a sliding seat, the abutting fixing rod is installed on the base in a sliding mode, the sliding seat is movably installed on the base, and the abutting fixing rod is connected with the sliding seat. And a mounting seat is arranged on the sliding seat. According to the photovoltaic station intelligent inspection tour device provided by the utility model, when the photovoltaic station is inspected, the inspection tour device such as an optical camera shooting device and an infrared temperature detection device can be replaced through the mounting seat, and the inspection tour device is fixed on the unmanned aerial vehicle through the fixing port and a bolt according to needs. The unmanned aerial vehicle can be operated to carry out flight inspection in the station to obtain related information, in continuous inspection work, equipment on the mounting base is directly replaced, the next round of inspection work can be carried out, various conditions in the photovoltaic station can be rapidly known, and use is convenient.

The invention discloses an indoor unmanned aerial vehicle rapid routing inspection navigation method. The method comprises the following steps: S1, dividing indoor routing inspection areas; S2, setting a routing inspection sequence of the indoor routing inspection areas; S3, setting a routing inspection path of an unmanned aerial vehicle in each indoor routing inspection area; according to the invention, the indoor space is divided into a plurality of indoor inspection areas, the inspection sequence of the indoor inspection areas is set, fixed object survey is carried out on each indoor inspection area, an inspection path for avoiding fixed objects and inspecting each inspection point is set, and the unmanned aerial vehicle carries out flight inspection according to the set inspection sequence and inspection path. The workload of surveying and flight route setting of the unmanned aerial vehicle and the upper computer in the flight process of the unmanned aerial vehicle is reduced, the inspection navigation efficiency of the unmanned aerial vehicle is improved, and the situation that inspection points are omitted in the inspection process of the unmanned aerial vehicle is avoided.

The invention belongs to the technical field of aircraft control, and relates to a pre-flight inspection method for a four-axis main joystick, which comprises the following steps of: 1, state initialization: setting the total state of the main joystick as a periodic task, and reporting the periodic task to a flight control computer through a bus; step 2, judging whether a pre-navigation check starting condition is met or not, and the process is as follows: S11, if bus communication is normal and the CCDL has no fault, entering S12; otherwise, exiting; s12, if the air / ground state dual-redundancy voting value is ground, the flight control system pre-flight inspection state dual-redundancy voting value is pre-flight, and the pre-flight inspection input switch dual-redundancy voting value is effective, the main control lever pre-flight inspection starting condition is met, and the step S13 is executed; otherwise, if the starting condition of the pre-navigation check of the main joystick is not met, entering S14; s13, setting the total state of the main joystick as pre-flight inspection, and reporting to a flight control computer through a bus; and S14, pre-flight effective mark setting, and reporting to a flight control computer through a bus. And if so, carrying out pre-flight inspection on the control shaft, otherwise, exiting.

The present invention provides a system and method for verifying the accuracy of a circular array radar's omnidirectional array by using a single-direction flight of an unmanned aerial vehicle (UAV). By using an automatic control turntable and a data acquisition and analysis system, the accuracy of the circular array radar's omnidirectional array is verified by flying the UAV in a fixed direction. This reduces restrictions on the radar's installation environment, avoids problems and difficulties associated with multi-directional flight inspections of UAVs, improves test efficiency, and shortens the test cycle.

The application discloses a kind of unmanned plane line simulation flight waypoint search method, route planning method and system, comprising: first, the center line discrete point sequence of the inspection road is obtained, and the waypoint spacing of line simulation flight route is set.Then according to the center line discrete point sequence, the direction vector of the to-be-searched polyline segment is determined.After that, according to the current waypoint coordinate, direction vector and waypoint spacing, the coordinates of candidate waypoint are determined, and whether the candidate waypoint is on the to-be-searched polyline segment is verified.If yes, the candidate waypoint is included in the waypoint set, and the candidate waypoint is used as the current waypoint to continue searching the next waypoint.If no, the remaining distance that needs to be moved after moving to the end point of the to-be-searched polyline segment is calculated, and the target line segment is searched out.Finally, the waypoint that meets the waypoint spacing is searched out by combining the direction vector and starting point coordinate of the target line segment, the remaining distance, so as to automatically generate line simulation inspection waypoint, improve the line simulation flight inspection route planning efficiency of long linear road unmanned plane.

The invention provides an unmanned aerial vehicle with foldable wings. The unmanned aerial vehicle comprises a fuselage assembly, the foldable wings, rotors and an anti-collision mechanism, the foldable wings are hinged to the fuselage assembly, the rotors are arranged on the foldable wings, and the anti-collision mechanisms are arranged on the rotors; when flight inspection is carried out in a narrow space (such as the interior of a building, a bridge pier, a tunnel and a cargo hold), the rotor wing can be protected through the anti-collision mechanism, and the rotor wing is prevented from being damaged.

The invention provides an unmanned aerial vehicle transformer substation inspection multi-machine cooperation method and system. According to the implementation scheme, under the condition of abnormal phase change of the main transformer, a temperature rise propagation path, a discharge interference propagation path and a vibration propagation path of the main transformer in the transformer substation are determined respectively based on temperature rise information, discharge interference information and vibration sudden change information of each core component; based on the temperature rise propagation path, the discharge interference propagation path and the vibration propagation path, the flight path of the temperature detection unmanned aerial vehicle, the flight path of the discharge detection unmanned aerial vehicle and the flight path of the vibration detection unmanned aerial vehicle are optimized; obtaining a target flight path of the temperature detection unmanned aerial vehicle, a target flight path of the discharge detection unmanned aerial vehicle and a target flight path of the vibration detection unmanned aerial vehicle, and respectively controlling the temperature detection unmanned aerial vehicle, the discharge detection unmanned aerial vehicle and the vibration detection unmanned aerial vehicle to carry out flight inspection. According to the invention, the inspection accuracy and efficiency can be improved.

The invention discloses machine room dual-mode inspection equipment and a machine room dual-mode inspection switching method, and relates to the technical field of machine room inspection, the machine room dual-mode inspection equipment comprises a track body hung on a ceiling, the track body is in sliding fit with an inspection main body, the bottom of the inspection main body is provided with an execution mechanism, and the tail end of the execution mechanism is connected with a camera shooting holder. Through a dual-mode switching structure of the track body and the unmanned aerial vehicle, the seamless switching of the hanging track inspection and the flight inspection is realized by combining a control separation and visual positioning technology, and the unmanned aerial vehicle has the advantages that the deployment cost is reduced, the coverage blind area is eliminated, and the inspection flexibility is improved.

The application discloses a kind of based on reinforcement learning's building unmanned plane inspection route intelligent planning method, comprising the following steps: collecting building facade contour information data, generates three-dimensional semantic model;Based on three-dimensional semantic model, simulation environment is constructed, and dynamic inspection virtual scene is formed;Hierarchical reinforcement learning model is established in dynamic inspection virtual scene, and multiple candidate path set is formed;Introduce voxel hash ray space index, output route ruling document;Based on route ruling document, form continuous controllable executable track set;Executable track set is imported into unmanned plane control system, and generates task preparation file;According to task preparation file, route flight inspection task is executed, and task execution log is output;Task execution log is uploaded to cloud, track deviation is analyzed and counted, and intelligent planning and adaptive closed-loop evolution are realized.The application realizes building unmanned plane inspection route intelligent planning.

The utility model relates to a kind of based on MESH network's intelligent inspection unmanned aerial vehicle applied to comprehensive pipe gallery, and it is related to inspection unmanned aerial vehicle field, including: unmanned aerial vehicle body;Observation equipment is used to gather the multi-source information of unmanned aerial vehicle in comprehensive pipe gallery;Center controller realizes the control to observation equipment;MESH network communication equipment is used for the transfer of information;MESH network transfer equipment is used as transfer node, so that unmanned aerial vehicle keeps and remote network connection;When unmanned aerial vehicle is in flight inspection, carry out three-dimensional coordinate positioning by laying MESH node in pipe gallery interior;KNN algorithm is used to optimize unmanned aerial vehicle inspection route;Information is transferred using MESH network, and single node failure does not lead to data transmission failure, that is, the utility model has self-organizing self-recovery capability.The positioning effect of the inspection unmanned aerial vehicle is better, and information transmission is stable, and adaptability is stronger.

The invention belongs to the technical field of path planning, and more specifically relates to a limited space unmanned aerial vehicle bridge simulation inspection path planning method. The method comprises the following steps: sensing and subscribing point cloud information of an environment by an unmanned aerial vehicle, wherein the point cloud information comes from a laser radar, a depth camera or a fusion point cloud; filtering: carrying out point cloud noisy point self-adaptive multi-level filtering on the point cloud information, executing nine-step self-adaptive noisy point filtering, and outputting filtered point cloud and a free space F; planning and bridge-imitating inspection path generation are carried out, an initial path is generated through a narrow channel RRT *, a safe flight inspection corridor SFIC is constructed, and a middle bridge-imitating inspection path is generated in the SFIC in an optimized mode. The problems of insufficient environmental perception precision, low safety and efficiency of path planning, lack of dynamic adaptability of path planning and low path quality during the inspection process of an existing unmanned aerial vehicle in a limited space in a bridge are solved.

The present invention provides a multi-machine collaborative method and system for UAV substation inspection. The implementation scheme is as follows: in the case of abnormal phase transformation of the main transformer, based on the temperature rise information, discharge interference information and vibration mutation information of each core component, the temperature rise propagation path, discharge interference propagation path and vibration propagation path of the main transformer in the substation are determined respectively; based on the temperature rise propagation path, discharge interference propagation path and vibration propagation path, the flight trajectory of the temperature detection UAV, the flight trajectory of the discharge detection UAV and the flight trajectory of the vibration detection UAV are optimized to obtain the target flight trajectory of the temperature detection UAV, the target flight trajectory of the discharge detection UAV and the target flight trajectory of the vibration detection UAV, and the temperature detection UAV, the discharge detection UAV and the vibration detection UAV are controlled respectively to perform flight inspection. The adoption of the present invention can improve the accuracy and efficiency of inspection.

The present invention relates to the technical field of transmission tower inspection, and discloses a method for inspecting a transmission tower with six cameras by an unmanned aerial vehicle and analyzing defects in target detection. By simply marking in the three-dimensional tower point cloud, the flight points for six cameras on one tower are calculated and generated; the flight points are connected in sequence to generate the inspection route of the unmanned aerial vehicle for six cameras on one tower; it is judged whether the route is safe, and turning points are automatically added to the unsafe route, so as to obtain the final inspection route of the tower unmanned aerial vehicle; the actual flight inspection is carried out, and the flight points are optimized: the yolov5 or Cornernet-Compare target detection algorithm is adopted to intelligently identify potential hazards in the inspection photos. The present invention can quickly generate the route for six cameras on one tower, improve the intelligent recognition algorithm, quickly identify defects and potential hazards in the inspection photos, and can find out defects and potential hazards faster and more accurately, improving the quality of data processing.

The invention discloses a highway intelligent inspection and abnormal state processing method based on an unmanned aerial vehicle, and belongs to the field of image processing and computer vision, and the method comprises the steps: carrying out the semantic disassembly and knowledge graph association retrieval of natural language input, and obtaining a situational inspection instruction; basic geographic information and a real-time dynamic environment are combined, and a dynamic safety corridor is generated through three-dimensional potential field repulsive force calculation; a sensor configuration scheme and a flight mode are extracted according to the associated scene features and mapped to a waypoint, and an optimized flight inspection task is generated; performing anomaly screening and fine identification on the video stream to generate anomaly confirmation information; and updating the knowledge graph and the configuration optimization relation by using the abnormal information and the disposal feedback data. According to the method, a closed-loop control technology integrating the knowledge graph, dynamic path planning and real-time image analysis is adopted, autonomous planning, accurate execution and intelligent feedback learning of the inspection task can be realized, and the utilization efficiency of image data in the inspection process and the accuracy of anomaly recognition are improved.

An embodiment of the present application provides an adaptive detection and detection system for low-altitude flight inspection, aiming to improve the efficiency and safety of UAV inspection operations. First, multiple UAV description information sets, UAV cluster description information, and flight inspection task description information are obtained. These information cover the performance parameters and task requirements of UAVs in different inspection scenarios. Subsequently, this information is loaded into the target adaptive inspection decision model of pre-knowledge learning. By analyzing this information, a target estimated confidence level is generated to evaluate the success probability of the UAV cluster when performing a specific inspection task. When the target estimated confidence level meets the set requirements, the corresponding UAV cluster is intelligently scheduled to perform the inspection task, thereby ensuring the efficient completion of the task. Thus, through intelligent evaluation and dynamic scheduling, the adaptability and flexibility of UAV inspection operations are effectively improved.

The invention provides a ground-air cooperative inspection method and system for oil depot inspection, and the method comprises the steps: determining a corresponding inspection position based on the coordinates of a selected inspection point in a pre-constructed three-dimensional model of an oil depot; determining an unmanned aerial vehicle task set and a ground task set based on the inspection position corresponding to each coordinate; constructing a ground inspection route based on the ground task set and the parking position of the inspection robot; constructing a flight inspection route based on the ground inspection route and the unmanned aerial vehicle task set, wherein the flight inspection route comprises a ground inspection route and a target inspection route; carrying out image recognition based on an image returned by the unmanned aerial vehicle in the flight process of the ground patrol route, and judging whether the corresponding sub-route is blocked or not; and if the sub-route is blocked, marking the sub-route, and updating the ground inspection route, so that the inspection robot performs inspection based on the updated ground inspection route.

The embodiment of the application discloses a kind of intelligent cable inspection systems, the system includes: complexity determination module is configured to receive the area image photographed in unmanned aerial vehicle inspection process, the area image is identified to obtain area complexity, the area complexity includes image complexity and line complexity;Parameter determination module is configured to determine the inspection parameter of the unmanned aerial vehicle according to the area complexity, the inspection parameter includes flight height, flight speed, flight direction and image shooting accuracy;Control module is configured to send the inspection parameter to the unmanned aerial vehicle, to control the unmanned aerial vehicle according to the inspection parameter to carry out the shooting of area image.The scheme improves the flight inspection efficiency of unmanned aerial vehicle, and possible line fault can be found more timely.

The present application proposes a helicopter snow blindness visibility measurement method based on image processing, belonging to the field of helicopter flight test. The purpose is to solve the problem that the existing helicopter visibility measurement in the high-cold snow area is mainly based on the subjective and difficult to quantify continuous monitoring artificial observation method, which cannot meet the automatic and objective measurement requirements under this special working condition. The present application comprises the following steps: S1 selecting test environment and test site that meet the test conditions; S2 determining the flight condition; S3 test flight practice; S4, installing test equipment; S5 judging the snow condition; S6 pre-flight inspection; S7 take-off test; S8 landing test; S9 test end inspection of helicopter state; S10 confirming whether there is remaining experiment; S11 image processing after the end; S12 data analysis. The present application solves the problem that the existing technology is mainly based on subjective and difficult to quantify continuous monitoring artificial observation method, which cannot meet the automatic and objective measurement requirements under this special working condition.

The present invention provides a multimodal aircraft that can be used for pipeline surveying, belonging to the field of multimodal unmanned aerial vehicles. The multimodal aircraft comprises a foldable rotor mechanism, an auxiliary wheel mechanism, a Mecanum wheel ground running mechanism, and a fuselage detection mechanism. The Mecanum wheel ground running mechanisms are symmetrically mounted at both ends of the fuselage detection mechanism, each Mecanum wheel ground running mechanism is connected to a foldable rotor mechanism on the outside, and the two auxiliary wheel mechanisms are symmetrically rotatably connected to the fuselage detection mechanism. After the auxiliary wheel mechanism is deployed, the two Mecanum wheel ground running mechanisms rotate in the same direction to enable the multimodal aircraft to run on the ground. The two Mecanum wheel ground running mechanisms rotate in opposite directions to enable the multimodal aircraft to run in a pipeline. After the foldable rotor mechanism is deployed, the multimodal aircraft can fly in the air. A detection unit mounted on the fuselage detection mechanism can detect the pipeline. The present invention can realize flight inspection, ground inspection, and pipeline inspection, and is easy to use.

Embodiments of the present application provide a pre-flight inspection method, device, equipment and storage medium, wherein the method comprises: when a flight body is in a pre-flight inspection stage of a control mechanism, controlling a display device associated with the flight body to display an inspection guide interface of the control mechanism; the inspection guide interface displays a control guide animation of the control mechanism and corresponding prompt information; when response data fed back by the control mechanism being inspected is received, determining whether a current inspection operation of the control mechanism is consistent with an inspection operation prompted by a current inspection guide interface according to the response data; when the current inspection operation of the control mechanism is consistent with the inspection operation prompted by the current inspection guide interface, and the current inspection guide interface is not the last inspection guide interface in the pre-flight inspection stage, controlling the display device to display a next inspection guide interface of the control mechanism.

The embodiment of the invention provides a rail low-altitude inspection air-rail dual-purpose unmanned inspection device and method, and the device comprises a rail driving module which is used for driving under the condition of different rail gauges and slightly uneven rails in a rail driving inspection mode; the flight module is used for carrying out flight operation in a flight inspection mode; the module switching node is used for switching the unmanned aerial vehicle from a flight inspection mode to a track driving inspection mode; the energy management module is used for performing electric energy distribution and real-time electric quantity monitoring in an operation mode; the intelligent decision-making and sensing fusion module is used for acquiring sensor data during inspection, performing efficient fusion and intelligent response on multi-source information in a track environment based on the sensor data, and performing data communication with a ground station or a cloud end; accurate modeling and intelligent analysis are carried out on the surrounding environment, and path planning is carried out on the track driving module and the flight module.

Generally discussed herein are systems, apparatuses, and methods for aiding a pre-flight inspection of an aircraft. This includes a system with processor, a memory, a camera, a user interface and a communications device. The user interface can include a user input system and a user output system. The user interface can be configured for presenting a graphical checklist for the pre-flight inspection of the aircraft. The memory includes, instructions, the instructions, when executed by the processor, cause the processor to determine a result of the pre-flight inspection for display on the graphical checklist, wherein the result is based upon at least a first data obtained from imaging a first onboard system of aircraft using the camera.

The present disclosure provides a kind of inspection path planning method and device for operating wind turbine, it is related to control technical field.The method comprises: in the online inspection stage, according to the mapping relationship generated in advance in offline stage, the maximum displacement value of blade tip under current operating condition is generated;Based on operating signal, the rigid kinematic model of blade is constructed, and the basic no-fly zone is determined;Dynamic safety radius is determined;With dynamic safety radius as inflation distance, the basic no-fly zone is outwardly inflated and handled, and the remaining space after removing dynamic no-fly zone in the preset flight space is determined as three-dimensional safe flight space;The three-dimensional inspection path of unmanned aerial vehicle is planned under the constraint of three-dimensional safe flight space, and the three-dimensional safe flight space is updated in the process of unmanned aerial vehicle flight inspection, and flight trajectory is adjusted.Based on the technical scheme, the automatic inspection path planning of operating wind turbine can be carried out in the inspection process.

The present invention relates to the field of material counting. The present invention discloses a method for counting materials for distribution poles and towers, which includes the following steps: Step 1: Use a drone to collect data of distribution poles and towers; Step 2: Mark the detection frames of the materials in each picture in D to obtain the detection data set Ddet; Step 3: Mark the material detection frames in each pole and tower in D; Step 5: Use the Dmatch data to train the cross-view frame matching model Match; Step 6: Obtain the final number of each material; The present invention utilizes computer vision for counting, realizes the material counting function based on drone flight inspection, avoids problems such as slow efficiency and misjudgment in manual counting, uses a drone to fly around the distribution pole and tower to collect data, and the included angle between adjacent pictures is not greater than θ degrees, ensuring the full coverage of the materials around the pole and tower, being able to obtain the image information of all angles of the pole and tower, avoiding the omission of materials caused by perspective limitations, having high collection efficiency and comprehensive data, and laying a solid foundation for subsequent accurate counting.

The embodiment of the invention discloses a bridge internal disease detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: deploying local positioning beacons around a to-be-detected bridge, building a local positioning system, and calculating the coordinates of the unmanned aerial vehicle; a mechanical arm is installed on the unmanned aerial vehicle, the unmanned aerial vehicle is controlled to conduct flight inspection according to the calculated coordinates so as to conduct non-contact visual inspection on the bridge, and visual inspection image data are obtained; constructing a disease detection model based on deep learning, performing typical bridge disease identification on the image obtained by visual detection by using the disease detection model, and positioning the position of each disease on the bridge according to an identification result; and the unmanned aerial vehicle flies to the identified bridge disease position, is attached to the bridge disease position through a mechanical arm on the unmanned aerial vehicle, and performs contact type internal flaw detection on the bridge by means of the mechanical arm so as to detect the disease condition in the bridge. According to the invention, apparent diseases and internal information of in-service structures such as bridges and the like can be rapidly detected.