CN117002748A - Aircraft defect detection method, system, equipment and medium - Google Patents
Aircraft defect detection method, system, equipment and medium Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64F—GROUND OR AIRCRAFT-CARRIER-DECK INSTALLATIONS SPECIALLY ADAPTED FOR USE IN CONNECTION WITH AIRCRAFT; DESIGNING, MANUFACTURING, ASSEMBLING, CLEANING, MAINTAINING OR REPAIRING AIRCRAFT, NOT OTHERWISE PROVIDED FOR; HANDLING, TRANSPORTING, TESTING OR INSPECTING AIRCRAFT COMPONENTS, NOT OTHERWISE PROVIDED FOR
- B64F5/00—Designing, manufacturing, assembling, cleaning, maintaining or repairing aircraft, not otherwise provided for; Handling, transporting, testing or inspecting aircraft components, not otherwise provided for
- B64F5/60—Testing or inspecting aircraft components or systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8806—Specially adapted optical and illumination features
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
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Abstract
The disclosure provides a method, a system, equipment and a medium for detecting aircraft defects, and relates to the technical field of defect detection. The method comprises the following steps: positioning the aircraft to be detected to obtain real-time position information; according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point. According to the method and the device, the aircraft to be detected is positioned, so that accurate matching of the automatic flight route of the unmanned aerial vehicle and the actual parking geographical position of the aircraft is ensured, the unmanned aerial vehicle can be started to automatically fly around the aircraft by one key, the upper surface picture of the aircraft is automatically shot, and therefore the defect detection efficiency of the aircraft is improved.
Description
Technical Field
The present disclosure relates to the field of defect detection technology, and in particular, to an aircraft defect detection method, an aircraft defect detection system, an electronic device, and a computer readable storage medium.
Background
Before leaving the field, maintenance personnel need to encircle the aircraft for one circle to complete the inspection of the outside of the aircraft, so that the aircraft is ensured to be in a state suitable for flying, and no maintenance omission exists.
In the related art, an inspection method of an upper surface of an aircraft performs a visual inspection for a serviceman to drive a lifting platform car due to the altitude of the aircraft. However, this inspection method has problems of poor effect and high omission ratio.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure provides a method, a system, a device and a medium for detecting an aircraft defect, which at least overcome the problems of poor effect and high omission rate of upper surface inspection of an aircraft in the related art to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided an aircraft defect detection method comprising: positioning the aircraft to be detected to obtain real-time position information; according to preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned plane to acquire images of the aircraft to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point.
In one embodiment of the disclosure, the real-time position information includes at least one of a position coordinate of a left main landing gear of the aircraft to be detected corresponding to a left rear wheel, a position coordinate of a right main landing gear corresponding to a right rear wheel, and a position coordinate of a front wheel in an unmanned aerial vehicle coordinate system; the method for positioning the aircraft to be detected to obtain real-time position information comprises the following steps: and positioning the aircraft to be detected by adopting a real-time dynamic RTK positioning mode to obtain the real-time position information.
In one embodiment of the present disclosure, the positioning the aircraft to be detected by using an RTK positioning method to obtain the real-time position information includes: acquiring position coordinates of any two wheels of the aircraft to be detected under the unmanned aerial vehicle coordinate system, wherein the any two wheels are any two of the left rear wheel, the right rear wheel and the front wheel; and determining the position coordinates of the rest wheels of the aircraft to be detected according to preset installation parameters and the position coordinates of any two wheels, and obtaining the real-time position information.
In one embodiment of the present disclosure, the preset position information of the photographing point includes at least one of a photographing point identification, a first distance between the photographing point and the left rear wheel, a second distance between the photographing point and the right rear wheel, and a third distance between the photographing point and the front wheel.
In one embodiment of the present disclosure, the determining, according to the preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight path, the corrected position information of each shooting point on the unmanned aerial vehicle flight path includes: and determining correction position information of each shooting point on the unmanned aerial vehicle flight route by adopting a maximum likelihood estimation method, a least square method or a triangle centroid algorithm, wherein the correction position information comprises position coordinates of the corresponding shooting point under an unmanned aerial vehicle coordinate system.
In one embodiment of the disclosure, each shooting point location has a plurality of shooting areas distributed in an array, and each shooting area corresponds to a set of operation parameters of the unmanned aerial vehicle; the controlling the unmanned aerial vehicle according to the corrected position information, collecting the image of the airplane to be detected at the corresponding shooting point position, comprising: controlling the unmanned aerial vehicle to a corresponding shooting point position; and determining operation parameters of the unmanned aerial vehicle based on the shooting area, shooting the corresponding shooting area to obtain an area image of the aircraft to be detected in the corresponding shooting area, and storing the area image in a server, wherein the storage information of the area image comprises shooting points to which the area image belongs and shooting area identifiers corresponding to the area image.
In one embodiment of the disclosure, the determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point location includes: and identifying the defects of the airplane to be detected in the images of the corresponding shooting points based on the pre-trained defect detection model.
According to another aspect of the disclosure, there is also provided an aircraft defect detection system, including an unmanned aerial vehicle and a controller, wherein the unmanned aerial vehicle is configured to locate an aircraft to be detected, and obtain real-time position information; the controller is used for determining correction position information of each shooting point on the unmanned aerial vehicle flight route according to preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned plane to acquire images of the aircraft to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the aircraft defect detection method described above via execution of the executable instructions.
According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-described aircraft defect detection method.
In the embodiment of the disclosure, an aircraft to be detected is positioned, and real-time position information is obtained; according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point. According to the method and the device, the aircraft to be detected is positioned, so that accurate matching of the automatic flight route of the unmanned aerial vehicle and the actual parking geographical position of the aircraft is ensured, the unmanned aerial vehicle can be started to automatically fly around the aircraft by one key, the upper surface picture of the aircraft is automatically shot, and therefore the defect detection efficiency of the aircraft is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure. It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a schematic diagram of an exemplary system architecture of an aircraft defect detection method in an embodiment of the present disclosure.
FIG. 2 illustrates a flow chart of a method for aircraft defect detection in an embodiment of the present disclosure.
Fig. 3 illustrates a schematic view of a flight path of a drone in an embodiment of the present disclosure.
Fig. 4 shows a schematic diagram of a drone scanning spot in an embodiment of the present disclosure.
FIG. 5 illustrates a flowchart of another aircraft defect detection method in an embodiment of the present disclosure.
Fig. 6 shows a flowchart of a method for locating an aircraft to be detected in an embodiment of the disclosure.
Fig. 7 illustrates a principle diagram of aircraft positioning to be detected in an embodiment of the disclosure.
Fig. 8 shows a flowchart of a corrected location information determination method in an embodiment of the present disclosure.
Fig. 9 illustrates a modified position information determination schematic diagram in an embodiment of the present disclosure.
Fig. 10 is a flowchart illustrating an area image capturing method of an aircraft to be detected in a corresponding capturing area in an embodiment of the disclosure.
Fig. 11 is a schematic diagram illustrating a plurality of shooting areas distributed in an array at a tail in an embodiment of the present disclosure.
Fig. 12 shows a schematic view of an area image acquired by each of the photographing areas in fig. 11.
FIG. 13 illustrates a schematic diagram of an aircraft defect detection system in an embodiment of the present disclosure.
FIG. 14 illustrates an interface schematic of an aircraft defect detection system in an embodiment of the present disclosure.
Fig. 15 shows a block diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
FIG. 1 illustrates a schematic diagram of an exemplary system architecture that may be applied to an aircraft defect detection method of an embodiment of the present disclosure.
As shown in fig. 1, the system architecture 100 may include a drone 110, a network 120, and a server 130.
The medium used by the network 120 to provide the communication link between the drone 110 and the server 130 may be a wireless network.
Optionally, the wireless network described above uses standard communication techniques and/or protocols. The network is typically the Internet, but may be any network including, but not limited to, a local area network (Local Area Network, LAN), metropolitan area network (Metropolitan Area Network, MAN), wide area network (Wide Area Network, WAN), mobile, wireless network, private network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including HyperText Mark-up Language (HTML), extensible markup Language (Extensible MarkupLanguage, XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as secure sockets layer (Secure Socket Layer, SSL), transport layer security (Transport Layer Security, TLS), virtual private network (Virtual Private Network, VPN), internet protocol security (Internet ProtocolSecurity, IPsec), etc. In other embodiments, custom and/or dedicated data communication techniques may also be used in place of or in addition to the data communication techniques described above.
The drone 110 may be a variety of electronic devices having RTK positioning functions and image acquisition functions.
Illustratively, the drone 110 configures an industrial-scale drone longitude and latitude RTK positioning system to enable positioning of the aircraft to be detected.
Illustratively, the unmanned aerial vehicle 110 is provided with a pan-tilt camera or other image acquisition device capable of capturing images of the surface of the aircraft to be inspected.
The server 130 may be a server that provides various services, such as a background management server that provides support for devices operated by the drone 110. The background management server may analyze and process the received data (e.g., the image of the surface of the aircraft to be detected collected by the unmanned aerial vehicle 110) to obtain a processing result (e.g., defect information of the surface of the aircraft to be detected).
Alternatively, the server 130 may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs (Content Delivery Network, content delivery networks), and basic cloud computing services such as big data and artificial intelligence platforms.
Those skilled in the art will appreciate that the number of drones, networks, servers in fig. 1 is merely illustrative, and that any number of drones, networks, servers may be provided as desired. The embodiments of the present disclosure are not limited in this regard.
In the related art, a serviceman steers a lifting platform car around an airplane for visual inspection. Due to the relatively large distance of the partial area of the aircraft from the inspector, for example the position of the top of the vertical tail, visual inspection is less effective and there may be missed inspections.
On the other hand, because the area of the upper surface area of the aircraft is large, no matter the unmanned aerial vehicle is driven to carry out the winding inspection, or the unmanned aerial vehicle is manually controlled to carry out the winding inspection, a great deal of labor cost and time cost are required to be consumed.
In order to solve at least a part of the problems, the present disclosure provides an aircraft defect detection method, which realizes that an unmanned aerial vehicle automatically flies around an aircraft, and automatically photographs an upper surface picture of the aircraft, thereby accurately identifying an aircraft defect.
According to the aircraft defect detection method, an aircraft to be detected is positioned, and real-time position information is obtained; according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point. According to the method and the device, the aircraft to be detected is positioned, so that accurate matching of the automatic flight route of the unmanned aerial vehicle and the actual parking geographical position of the aircraft is ensured, the unmanned aerial vehicle can be started to automatically fly around the aircraft by one key, the upper surface picture of the aircraft is automatically shot, and therefore the defect detection efficiency of the aircraft is improved.
The present exemplary embodiment will be described in detail below with reference to the accompanying drawings and examples.
First, in an embodiment of the present disclosure, an aircraft defect detection method is provided, which may be performed by any electronic device having computing processing capabilities. In some embodiments, the method may be performed by a drone; in other embodiments, the method may be performed by a server; in addition, the interaction between the unmanned aerial vehicle and the server can be realized.
Fig. 2 shows a flowchart of an aircraft defect detection method in an embodiment of the disclosure, and as shown in fig. 2, the aircraft defect detection method provided in the embodiment of the disclosure includes the following steps:
s202, positioning the aircraft to be detected to obtain real-time position information.
The aircraft to be detected is a shooting object of the unmanned aerial vehicle, namely a target which needs to be determined whether defects exist or not.
It should be noted that, the above real-time position information includes, but is not limited to, a position coordinate of the left main landing gear corresponding to the left rear wheel, a position coordinate of the right main landing gear corresponding to the right rear wheel, a position coordinate of the front wheel, a center position coordinate of the aircraft to be detected, and a yaw angle, where the center position coordinate of the aircraft to be detected may be a center coordinate of a triangle formed by the left rear wheel, the right rear wheel, and the front wheel.
The yaw angle mentioned above refers to the difference between the parking position of the aircraft to be detected relative to the preset position of the aircraft to be detected in memory.
S204, determining correction position information of each shooting point on the unmanned aerial vehicle flight route according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight route.
In one embodiment, when the unmanned aerial vehicle flies around the aircraft to be detected, due to different sizes of the aircraft to be detected of different models, the flight route of the unmanned aerial vehicle needs to be preconfigured for the aircraft to be detected, and the flight routes of the unmanned aerial vehicles corresponding to the aircraft to be detected of different models are different. As shown in fig. 3, an unmanned aerial vehicle flight path is provided, the flying spot of the unmanned aerial vehicle is arranged at the position close to the aircraft nose of the aircraft to be detected, the left side and the right side can be used as the flying spot, and the horizontal distance between the unmanned aerial vehicle flight path and the aircraft to be detected is kept above 5 meters, so that the safety of the unmanned aerial vehicle and the aircraft to be detected is determined.
As shown in fig. 3, one point in the nose position may be taken as a flying spot, and the upper surface of the aircraft to be detected may be photographed in a counterclockwise direction, or may be photographed in a clockwise direction.
In one embodiment, as shown in fig. 4, a plurality of shooting points, for example, 1 to 36 in fig. 4, may be set on the flight path of the unmanned aerial vehicle, where the plurality of shooting points correspond to different parts of the aircraft to be detected, so as to realize that a shooting area of the unmanned aerial vehicle can cover the upper surface of the aircraft to be detected, an image collected by selecting the shooting points can be sufficiently clear so that a user can distinguish the defect by naked eyes, and meanwhile, a distance between the unmanned aerial vehicle and the aircraft to be monitored is sufficiently far so as to ensure safety.
In one implementation manner, the preset position information of each shooting point on the unmanned aerial vehicle flight path may include at least one of a shooting point identifier, a first distance between the shooting point and the left rear wheel, a second distance between the shooting point and the right rear wheel, a third distance between the shooting point and the front wheel, a position coordinate of the shooting point, and a center position coordinate of the aircraft to be detected.
The shooting point position identifiers can be represented by characters, numbers, letters, symbols and the like, and the shooting point position identifiers are used for distinguishing different shooting point positions on the flight route of the unmanned aerial vehicle.
It should be noted that, preset position information of each shooting point on the flight route of the unmanned aerial vehicle may be stored in the server in advance, and by calling the preset position information and combining with real-time position information, correction position information of each shooting point is determined, where the correction position information is used for determining a position coordinate of each shooting point of the aircraft to be detected in the current parking position under the unmanned aerial vehicle coordinate system.
S206, controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information.
In one embodiment, a user controls the unmanned aerial vehicle through a software system interface, so that the unmanned aerial vehicle executes an automatic winding task, and the unmanned aerial vehicle moves to a corresponding shooting point according to the corrected position information obtained in the step S204, and acquires an image of the airplane to be detected.
The images of the aircraft to be detected, which are acquired by the unmanned aerial vehicle, can be stored in a server, and the images can be marked by adopting shooting points.
S208, determining defect information of the airplane to be detected according to the image of the airplane to be detected at the corresponding shooting point.
In one embodiment, whether the image of each shooting point is defective or not can be recognized by human eyes.
In some embodiments, defects in the image of the corresponding shot point of the aircraft to be detected may be identified based on a pre-trained defect detection model. Namely, whether defects exist in the image or not is identified by adopting a target defect detection mode. The defect detection model can be obtained through training of defect images collected by an aircraft to be detected in the repair or maintenance process.
The defect detection model may include, but is not limited to, convolutional neural network models, backbone network models. The mean square error or the cross entropy error can be used as a loss function for measuring the difference between the defect prediction result of the defect detection model and the defect label. The training process of the defect detection model is not described in detail.
In another embodiment, whether the images of the shooting points are defective or not may also be determined by comparing the images of the shooting points shot by the unmanned aerial vehicle with the non-defective images of the aircraft to be detected stored in advance in the memory.
The defect information of the aircraft to be detected can be divided into different defect grades according to the severity of the defects, and different processing modes are adopted according to the different defect grades.
For example, when the defect information of the aircraft to be detected is a first-level defect level, the first-level defect level may be a situation that the aircraft has a potential safety hazard and is unsuitable for flying, and at this time, a flight stopping control instruction is generated.
When the defect information of the aircraft to be detected is a secondary defect grade, the secondary defect grade can be the condition that maintenance is missed or the maintenance is not up to standard, and the like, and a maintainer is required to process the defects in a targeted manner.
When the defect information of the aircraft to be detected is three-level defect grade, wherein the three-level defect grade is a defect which does not affect the aircraft to be detected, the aircraft is suitable for flying and does not need to be processed.
It should be noted that the defect information of the aircraft to be detected may be set according to actual situations, and the disclosure is not limited specifically.
In the embodiment of the disclosure, an aircraft to be detected is positioned, and real-time position information is obtained; according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point. According to the method and the device, the aircraft to be detected is positioned, so that accurate matching of the automatic flight route of the unmanned aerial vehicle and the actual parking geographical position of the aircraft is ensured, the unmanned aerial vehicle can be started to automatically fly around the aircraft by one key, the upper surface picture of the aircraft is automatically shot, and therefore the defect detection efficiency of the aircraft is improved.
Fig. 5 shows a flowchart of another unmanned aerial vehicle defect detection method in an embodiment of the present disclosure. Based on the embodiment of fig. 2, S202 is further refined to S2022, so as to determine the positioning manner of the aircraft to be detected. As shown in fig. 5, the method for detecting defects of an unmanned aerial vehicle provided by the embodiment of the disclosure includes S2022, S204 to S208, and the method includes:
s2022, positioning the aircraft to be detected by adopting an RTK positioning mode to obtain real-time position information.
It should be noted that, the specific implementation manner of S204 to S208 in the present embodiment is the same as the specific implementation manner of S204 to S208 in the foregoing embodiment, and will not be repeated here.
In one embodiment, an RTK (Real-time kinematic) positioning system, also known as an RTK carrier phase difference technique, is onboard the drone. The RTK positioning system comprises a reference station and an rover, wherein the reference station and the rover both carry satellite receivers and can observe and receive satellite data, the reference station is a base station for providing a reference standard, and the reference station can be arranged on an unmanned plane; a rover is a station that can move continuously. The rover station is the object target to be measured for its own three-dimensional coordinates, i.e., the left main landing gear and the right main landing gear in the present disclosure.
At the time of measurement, the reference station is known as a measurement reference, and coordinate information of the reference station is known. The reference station observes and receives satellite data first, the reference is transferred to a nearby radio station (also called a data link), the observed data is sent to the mobile station in real time, and the mobile station observes and receives the satellite data while the mobile station receives the reference station observed data. The mobile station performs real-time differential operation according to the relative positioning principle on the basis of the reference station data and the self-observation data, so that the position coordinates and the accuracy of the mobile station are obtained through calculation.
In the embodiment of the disclosure, the unmanned aerial vehicle-mounted RTK positioning system is adopted to correct the automatic flight route of the unmanned aerial vehicle, so that the accurate matching of the automatic flight route of the unmanned aerial vehicle and the geographic position of the airplane to be detected is realized, and the accuracy of the airplane defect detection method is improved.
Fig. 6 shows a flowchart of a method for locating an aircraft to be detected in an embodiment of the disclosure. As shown in fig. 6, in one embodiment, S2022 locates the aircraft to be detected by using an RTK positioning method, and determining the position coordinates of the front wheel of the aircraft to be detected in the unmanned aerial vehicle coordinate system includes:
s602, acquiring position coordinates of any two wheels of an airplane to be detected under an unmanned aerial vehicle coordinate system, wherein the any two wheels are any two of a left rear wheel, a right rear wheel and a front wheel;
s604, determining the position coordinates of the rest wheels of the airplane to be detected according to the preset installation parameters and the position coordinates of any two wheels, and obtaining real-time position information.
Note that, the coordinate position of the left main landing gear and the acquisition sequence of the coordinate position of the right main landing gear are not related, that is, S602 may be performed before S604 or after S604.
In one embodiment, the position coordinates of any two of the left rear wheel, the right rear wheel and the front wheel of the aircraft to be detected under the unmanned aerial vehicle coordinate system can also be acquired by adopting an RTK positioning mode, and the position coordinate of the third wheel is calculated according to preset installation parameters to obtain real-time position information, so that a basis is provided for calculating the point position coordinates of the automatic winding machine.
The position coordinates of the left rear wheel and the right rear wheel of the aircraft to be detected in the unmanned plane coordinate system are acquired in an RTK positioning mode, and the position coordinates of the front wheel are calculated according to preset installation parameters and the position coordinates of the left rear wheel and the right rear wheel.
The position coordinates of the left rear wheel and the front wheel of the aircraft to be detected in the unmanned plane coordinate system are acquired in an RTK positioning mode, and the position coordinates of the right rear wheel are calculated according to preset installation parameters and the position coordinates of the left rear wheel and the front wheel.
It should be noted that any two wheels are preferably a left rear wheel and a right rear wheel, so that unmanned aerial vehicle operation is facilitated.
In one embodiment, the central position coordinates of the aircraft to be detected can be determined according to the position coordinates of the three wheels, so that real-time position information of the aircraft to be detected is obtained, a basis is provided for automatic calculation of point position coordinates around the aircraft, a coordinate transformation matrix can be determined according to a centroid algorithm, and then the position coordinates of the shooting point positions are obtained according to the coordinate transformation matrix and preset position coordinates of the shooting point positions.
As shown in fig. 7, the preset installation parameters in S606 include, but are not limited to, a distance d between the left main landing gear and the right main landing gear, a distance l between the front wheel and the connection line of the left rear road and the right rear wheel, an included angle α between the connection line of the front wheel and the left rear wheel and the connection line of the front wheel and the right rear wheel, and the like.
In one embodiment, the yaw angle of the aircraft to be detected may be determined from a trigonometric function relationship. Yaw angle θ is determined by:
θ=arctan ((lng 2-lng 1)/(lat 2-lat 1)) +pi/2 equation one
Wherein, θ e [0, pi ], θ plus or minus is determined by unmanned aerial vehicle orientation angle assistance, arctan () is an arctan function, (lat 1, lng 1) is the coordinate value of the left rear wheel under unmanned aerial vehicle coordinate system, and (lat 2, lng 2) is the coordinate value of the right rear wheel under unmanned aerial vehicle coordinate system.
Since d, l is known, the front wheel coordinates (lat 3, lng 3) can be calculated.
The corrected position information can be obtained by performing corresponding translation operation and rotation operation according to the real-time position information and yaw angle of the aircraft to be detected.
In one embodiment, the step S204 of determining the corrected position information of each shooting point on the unmanned aerial vehicle flight path according to the preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight path includes: s802, determining correction position information of each shooting point on the unmanned aerial vehicle flight route by adopting a maximum likelihood estimation method, a least square method or a triangle centroid algorithm, wherein the correction position information comprises position coordinates of the corresponding shooting point under an unmanned aerial vehicle coordinate system.
Fig. 9 illustrates a modified position information determination schematic diagram in an embodiment of the present disclosure. Real-time position determination of aircraft to be detectedAfter the position coordinates of the left main landing gear, the right main landing gear and the front wheel of the aircraft to be detected are determined, as shown in fig. 9, the position of the front wheel is designated as point C, and the position coordinates corresponding to point C are (x 1 ，y 1 ) The point B is the position of the left rear wheel, and the position coordinate of the point B is (x 2 ，y 2 ) The point A is the position of the right rear wheel, and the position coordinate of the point A is (x 3 ，y 3 ) The method comprises the steps of carrying out a first treatment on the surface of the The point D is a scanning point on the flight path of the unmanned aerial vehicle, and the coordinate of the point D is an unknown quantity.
Determining the position coordinates of the D point according to pre-stored preset position information of the shooting point of the D point, wherein the preset position information comprises a first distance R2 between the shooting point D point and the left rear wheel B point, a second distance R1 between the shooting point D point and the right rear wheel A point and a third distance R3 between the shooting point D point and the front wheel C point, the left rear wheel B point, the right rear wheel A point and the front wheel C point are respectively used as circle centers, the R2, the R1 and the R3 are used as radiuses, three circles can be obtained, the three circles are intersected, and the position coordinates of the shooting point D point are coordinates of intersection points of the three circles.
Because of errors in the measurements, the above circles may not intersect at a point, but rather may be solved by other algorithms than an area, such as maximum likelihood estimation, least squares estimation, or using triangle centroid algorithms.
The following describes the method of solving an approximation solution by using a least square method, and determining the position coordinates of the shooting point D. The solving steps are as follows:
establishing a distance equation set of the beacon node (point A, point B and point C) and the unknown node (point D):
wherein (x, y) is the position coordinate of the unknown node D point, (x) i ，y i ) Is the position coordinate of the ith beacon node, where i=1, 2 … n corresponds to the position coordinates of each beacon node (point a, point B and point C), respectively, in this disclosure n is3。
The distance equation set is a nonlinear equation set, and the n-1 equation is subtracted from the n-th equation to obtain a linearized equation:
ax=b equation two
Wherein,
the coordinates of one point location of the linearization equation are solved by adopting a least square method, as follows:
X＝(A T A) -1 A T b formula three
Thus, the position coordinates of the unknown node, namely the corrected position information, can be obtained.
In other embodiments, a triangle centroid algorithm may be used to determine the triangle center position coordinates of the real-time position information, determine a coordinate transformation matrix according to the pre-stored center position coordinates of the aircraft to be detected, and determine the corrected position coordinates of the shooting point according to the coordinate transformation matrix and the position coordinates of the shooting point.
In the embodiment of the disclosure, the correction position information of each shooting point on the unmanned aerial vehicle flight route is determined by adopting a maximum likelihood estimation method, a least square method or a triangle centroid algorithm, etc., the shooting point coordinates of the aircraft to be detected in the parking position are precisely determined, the position coordinates of each shooting point are estimated, and the accuracy of aircraft defect detection is improved.
It should be noted that, a plurality of shooting points are configured at each shooting point, each shooting point has a plurality of shooting areas distributed in an array, and each shooting area corresponds to a set of operation parameters of the unmanned aerial vehicle.
In order to complete shooting functions, the unmanned aerial vehicle carries a cradle head system, and when the unmanned aerial vehicle advances to a shooting point position, the cradle head can acquire regional images of the airplane to be detected in each shooting region by controlling the pitching rotation angle, the horizontal rotation angle and the like of the cradle head system.
Fig. 10 is a flowchart illustrating an area image capturing method of an aircraft to be detected in a corresponding capturing area in an embodiment of the disclosure. As shown in fig. 10, in an embodiment, the controlling unmanned aerial vehicle in S206 collects an image of the aircraft to be detected at the corresponding shooting point according to the corrected position information, including:
Controlling the unmanned aerial vehicle to a corresponding shooting point position;
and determining operation parameters of the unmanned aerial vehicle based on the shooting areas, shooting the corresponding shooting areas to obtain area images of the aircraft to be detected in the corresponding shooting areas, and storing the area images in a server, wherein the stored information of the area images comprises shooting points to which the area images belong and shooting area identifiers corresponding to the area images.
As shown in fig. 11, each shooting point is divided into a matrix, the upper surface of the aircraft to be detected corresponding to the shooting point is divided into a plurality of small shooting areas, in fig. 11, 36 shooting areas are set up in 4 rows and 9 columns, and each shooting area corresponds to one area of the horizontal tail of the aircraft to be detected.
In the unmanned aerial vehicle winding process, hovering at each shooting point position, the cradle head camera automatically shoots images of the shooting areas corresponding to the shooting point positions according to the operation parameters of the unmanned aerial vehicle determined by the shooting areas, and the array shooting effect is shown in fig. 12.
The Json file corresponding to each image shot by the cradle head camera records shooting points, the position of the shooting area in the array and other relevant information, and provides basis for the subsequent defect position positioning.
The shooting area identifier corresponding to the area image may be represented by a row number and a column number of the area image in the array, or may be represented by a shooting sequence number of the area image in a shooting point, which is not particularly limited in the disclosure.
In the embodiment of the disclosure, the surface of the aircraft to be detected at each shooting point is divided into a plurality of shooting areas, so that the defect position is quickly and conveniently confirmed, the upper surface of the aircraft to be detected is comprehensively covered by the shooting areas, the comprehensiveness of aircraft defect detection is improved, and the condition of missing detection is avoided.
In one embodiment, determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point in S208 includes: determining a preset area image according to shooting area identifications corresponding to shooting points and area images; comparing the preset area image with the area image, and determining defect information of the airplane to be detected in the area image of the shooting area.
It should be noted that, the preset area image is an image of the upper surface of the aircraft to be detected, which is not defective when the upper surface corresponds to the shooting area, and may be stored in the server in advance, and the storage information of the preset area image also includes shooting points and shooting area identifiers.
In the embodiment of the disclosure, the preset area image stored in the server is determined through the shooting point position and the shooting area identifier, and the defects in the area image can be determined according to the comparison result of the preset area image and the shot area image, so that the automatic detection of the aircraft defects is realized.
Based on the same inventive concept, an aircraft defect detection system is also provided in embodiments of the present disclosure, as described in the following embodiments. Since the principle of solving the problem of the system embodiment is similar to that of the method embodiment, the implementation of the system embodiment can be referred to the implementation of the method embodiment, and the repetition is omitted.
FIG. 13 illustrates a schematic diagram of an aircraft defect detection system in an embodiment of the disclosure, as illustrated in FIG. 13, in one embodiment an aircraft defect detection system includes: the drone 110 and a controller 1310.
The unmanned aerial vehicle 110 is used for positioning an aircraft to be detected to obtain real-time position information;
the controller 1310 is configured to determine corrected position information of each shooting point on the unmanned aerial vehicle flight path according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight path; controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point.
It should be noted that the real-time position information includes at least one of a position coordinate of the left main landing gear of the aircraft to be detected corresponding to the left rear wheel, a position coordinate of the right main landing gear corresponding to the right rear wheel, and a position coordinate of the front wheel in the unmanned plane coordinate system.
In one embodiment, the unmanned aerial vehicle 110 is configured to position the aircraft to be detected by using an RTK positioning manner, so as to obtain real-time position information.
In one embodiment, the unmanned aerial vehicle 110 is configured to collect, under an unmanned aerial vehicle coordinate system, position coordinates of any two wheels of an aircraft to be detected, where any two wheels are any two of a left rear wheel, a right rear wheel, and a front wheel; and determining the position coordinates of the rest wheels of the aircraft to be detected according to the preset installation parameters and the position coordinates of any two wheels to obtain real-time position information.
The preset position information of the shooting point location includes at least one of a shooting point location identifier, a first distance between the shooting point location and the left rear wheel, a second distance between the shooting point location and the right rear wheel, and a third distance between the shooting point location and the front wheel.
In one embodiment, the controller 1310 is configured to determine, using at least one of a maximum likelihood estimation method, a least squares method, or a triangle centroid algorithm, corrected location information of each shooting point on the unmanned aerial vehicle flight path, where the corrected location information includes location coordinates of the corresponding shooting point in the unmanned aerial vehicle coordinate system.
It should be noted that each shooting point location has a plurality of shooting areas distributed in an array, and each shooting area corresponds to a group of operation parameters of the unmanned aerial vehicle; a controller 1310, configured to control the unmanned aerial vehicle to a corresponding shooting point location; determining operation parameters of the unmanned aerial vehicle based on the shooting area; the unmanned aerial vehicle 110 is configured to shoot a corresponding shooting area, obtain an area image of the aircraft to be detected in the corresponding shooting area, and store the area image in the server, where the storage information of the area image includes shooting points to which the area image belongs and shooting area identifiers corresponding to the area image.
In one embodiment, the controller 1310 is configured to identify a defect in the image of the corresponding shot point of the aircraft to be detected based on a pre-trained defect detection model.
In an embodiment of the disclosure, positioning an aircraft to be detected to obtain real-time position information; according to preset position information and real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned aerial vehicle to acquire images of the airplane to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point. According to the method and the device, the aircraft to be detected is positioned, so that accurate matching of the automatic flight route of the unmanned aerial vehicle and the actual parking geographical position of the aircraft is ensured, the unmanned aerial vehicle can be started to automatically fly around the aircraft by one key, the upper surface picture of the aircraft is automatically shot, and therefore the defect detection efficiency of the aircraft is improved.
FIG. 14 illustrates an interface schematic of an aircraft defect detection system in an embodiment of the present disclosure. As shown in fig. 14, in the aspect of a control system, a set of software system for automatic flight around the unmanned aerial vehicle and automatic photographing is designed based on the unmanned aerial vehicle, and the interface of the software system is shown in fig. 14, and the software system interface can realize a photographing function, a cradle head function, a geofence function, a pause key function, a screenshot function, an automatic Next function, a left start function, a modified data function, a push rocker stopping task function, a cradle head fine tuning function, a fusion RTK function and the like. It should be noted that, for the first time, the software system needs to be authorized and logged into DJI account.
And during formal operation, the unmanned aerial vehicle operator performs RTK positioning on the left main landing gear and the right main landing gear of the airplane to be detected. After the collection and positioning, the unmanned aerial vehicle is placed near a departure point, and then the unmanned aerial vehicle can be started to fly around the aircraft automatically and automatically shoot pictures of the upper surface of the aircraft to be detected by one key.
The specific operation flow is as follows:
clicking an application icon, authorizing and logging in DJI account numbers, selecting airplane types and setting attribute parameters, wherein the set attribute parameters comprise, but are not limited to, airplane types to be detected, unmanned aerial vehicle forbidden information confirmation, unmanned aerial vehicle electronic fence information, unmanned aerial vehicle obstacle avoidance function switches and the like.
Clicking an FPV picture, moving the unmanned aerial vehicle to an open area, turning on an RTK function switch, carrying the unmanned aerial vehicle to the vicinity of an airplane to be detected after 1min of signal stabilization, collecting the position coordinates of a left main landing gear and a right main landing gear of the airplane to be detected under an unmanned aerial vehicle coordinate system, and clicking to generate a route coordinate after the position coordinates of the left main landing gear and the right main landing gear are collected.
And moving the unmanned aerial vehicle to the vicinity of the departure point, clicking a start button to start an airline task after the RTK signal is stable, checking the return configuration and the electric quantity, and clicking to determine.
Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
An electronic device 1500 according to such an embodiment of the present disclosure is described below with reference to fig. 15. The electronic device 1500 shown in fig. 15 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
As shown in fig. 15, the electronic device 1500 is embodied in the form of a general purpose computing device. The components of electronic device 1500 may include, but are not limited to: the at least one processing unit 1510, the at least one storage unit 1520, a bus 1530 that connects the different system components (including the storage unit 1520 and the processing unit 1510).
Wherein the storage unit stores program code that is executable by the processing unit 1510 such that the processing unit 1510 performs steps according to various exemplary embodiments of the present disclosure described in the above section of the "exemplary method" of the present specification.
By way of example, the processing unit 1510 may perform the following steps of the method embodiment as in fig. 2: positioning the aircraft to be detected to obtain real-time position information; according to preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned plane to acquire images of the aircraft to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point.
The storage unit 1520 may include readable media in the form of volatile memory units such as Random Access Memory (RAM) 15201 and/or cache memory 15202, and may further include Read Only Memory (ROM) 15203.
The storage unit 1520 may also include a program/utility 15204 having a set (at least one) of program modules 15205, such program modules 15205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 1530 may be a bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 1500 may also communicate with one or more external devices 1540 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 1500, and/or any device (e.g., router, modem, etc.) that enables the electronic device 1500 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1550. Also, the electronic device 1500 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, for example, the Internet, through a network adapter 1560. As shown, the network adapter 1560 communicates with other modules of the electronic device 1500 over the bus 1530. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1500, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the above description of embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a terminal device, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, a computer-readable storage medium, which may be a readable signal medium or a readable storage medium, is also provided. The computer readable storage medium has stored thereon a program product capable of implementing the above-described method of the present disclosure. In some possible implementations, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a user terminal to carry out the steps according to the various exemplary embodiments of the disclosure as described in the "exemplary methods" section of this specification, when the program product is run on the user terminal.
More specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In this disclosure, a computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Alternatively, the program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In particular implementations, the program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
From the description of the above embodiments, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. Thus, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.) or on a network, including several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present disclosure.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. An aircraft defect detection method, comprising:
positioning the aircraft to be detected to obtain real-time position information;
according to preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight route, determining correction position information of each shooting point on the unmanned aerial vehicle flight route;
controlling the unmanned plane to acquire images of the aircraft to be detected at corresponding shooting points according to the corrected position information;
and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point.
2. The method of claim 1, wherein the real-time position information comprises at least one of position coordinates of a left main landing gear of the aircraft to be detected corresponding to a left rear wheel, position coordinates of a right main landing gear corresponding to a right rear wheel, and position coordinates of a front wheel in an unmanned aerial vehicle coordinate system;
the method for positioning the aircraft to be detected to obtain real-time position information comprises the following steps:
and positioning the aircraft to be detected by adopting a real-time dynamic RTK positioning mode to obtain the real-time position information.
3. The method of claim 2, wherein positioning the aircraft to be inspected using an RTK positioning method to obtain the real-time location information comprises:
Acquiring position coordinates of any two wheels of the aircraft to be detected under the unmanned aerial vehicle coordinate system, wherein the any two wheels are any two of the left rear wheel, the right rear wheel and the front wheel;
and determining the position coordinates of the rest wheels of the aircraft to be detected according to preset installation parameters and the position coordinates of any two wheels, and obtaining the real-time position information.
4. The method of claim 2, wherein the preset location information of the photographing point includes at least one of a photographing point identification, a first distance between the photographing point and the left rear wheel, a second distance between the photographing point and the right rear wheel, and a third distance between the photographing point and the front wheel.
5. The method according to claim 4, wherein determining the corrected position information of each shooting point on the unmanned aerial vehicle flight path according to the preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight path comprises:
and determining correction position information of each shooting point on the unmanned aerial vehicle flight route by adopting a maximum likelihood estimation method, a least square method or a triangle centroid algorithm, wherein the correction position information comprises position coordinates of the corresponding shooting point under an unmanned aerial vehicle coordinate system.
6. The method of claim 1, wherein each of the plurality of shooting points has a plurality of shooting areas distributed in an array, each shooting area corresponding to a set of operating parameters of the drone;
the controlling the unmanned aerial vehicle according to the corrected position information, collecting the image of the airplane to be detected at the corresponding shooting point position, comprising:
controlling the unmanned aerial vehicle to a corresponding shooting point position;
and determining operation parameters of the unmanned aerial vehicle based on the shooting area, shooting the corresponding shooting area to obtain an area image of the aircraft to be detected in the corresponding shooting area, and storing the area image in a server, wherein the storage information of the area image comprises shooting points to which the area image belongs and shooting area identifiers corresponding to the area image.
7. The method according to any one of claims 1-6, wherein determining defect information of the aircraft to be detected based on the image of the aircraft to be detected at the corresponding shooting point location comprises:
and identifying the defects of the airplane to be detected in the images of the corresponding shooting points based on the pre-trained defect detection model.
8. An aircraft defect detection system, comprising an unmanned aerial vehicle and a controller, wherein,
The unmanned aerial vehicle is used for positioning the aircraft to be detected to obtain real-time position information;
the controller is used for determining correction position information of each shooting point on the unmanned aerial vehicle flight route according to preset position information and the real-time position information of each shooting point on the unmanned aerial vehicle flight route; controlling the unmanned plane to acquire images of the aircraft to be detected at corresponding shooting points according to the corrected position information; and determining defect information of the aircraft to be detected according to the image of the aircraft to be detected at the corresponding shooting point.
9. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the aircraft defect detection method of any one of claims 1-7 via execution of the executable instructions.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the aircraft defect detection method according to any one of claims 1 to 7.
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