CN117975313A - Unmanned aerial vehicle state detection method, unmanned aerial vehicle control device and unmanned aerial vehicle - Google Patents
Unmanned aerial vehicle state detection method, unmanned aerial vehicle control device and unmanned aerial vehicle Download PDFInfo
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Abstract
The application discloses an unmanned aerial vehicle state detection method, an unmanned aerial vehicle control device and an unmanned aerial vehicle, wherein a camera device capable of collecting images at a horn of the unmanned aerial vehicle is arranged on the unmanned aerial vehicle, and the unmanned aerial vehicle state detection method comprises the following steps: acquiring an image to be identified acquired by a camera device, and determining the detection position of the unmanned aerial vehicle arm in the image to be identified; acquiring a calibration area of the unmanned aerial vehicle arm in a calibration image, and determining whether the unmanned aerial vehicle arm reaches an unfolding state or not based on the calibration area and the detection position; the calibration image is an image acquired when the unmanned aerial vehicle arm reaches an unfolding state. Through the mode, the reliability of unmanned aerial vehicle state detection can be improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle state detection method, an unmanned aerial vehicle control device and an unmanned aerial vehicle.
Background
In order to save unmanned aerial vehicle's accommodation space, conventional unmanned aerial vehicle horn is folding horn, adjusts unmanned aerial vehicle horn to the expansion state when needs use unmanned aerial vehicle to carry out the task. In the prior art, a sensor is generally used for acquiring a positioning signal to position an unmanned aerial vehicle arm, so that the distance between the unmanned aerial vehicle arms is calculated based on positioning information to judge whether the unmanned aerial vehicle arm reaches an unfolding state, but the detection result is unreliable when the quality of the positioning signal is poor, and if the unmanned aerial vehicle arm fails to reach the unfolding state, the unmanned aerial vehicle arm is put into use, and the unmanned aerial vehicle is very likely to cause task delay or even damage. In view of this, how to improve the reliability of unmanned aerial vehicle state detection is a problem to be solved.
Disclosure of Invention
The application mainly solves the technical problem of providing an unmanned aerial vehicle state detection method, an unmanned aerial vehicle control device and an unmanned aerial vehicle, and can improve the reliability of unmanned aerial vehicle state detection.
To solve the above technical problem, a first aspect of the present application provides a method for detecting a state of an unmanned aerial vehicle, where an image capturing device capable of capturing an image at a horn of the unmanned aerial vehicle is disposed on the unmanned aerial vehicle, the method comprising: acquiring an image to be identified acquired by the camera device, and determining the detection position of the unmanned aerial vehicle arm in the image to be identified; acquiring a calibration area of the unmanned aerial vehicle arm in a calibration image, and determining whether the unmanned aerial vehicle arm reaches an unfolding state or not based on the calibration area and the detection position; the calibration image is an image acquired when the unmanned aerial vehicle arm reaches the unfolding state.
To solve the above technical problem, a second aspect of the present application provides an unmanned aerial vehicle control device, including: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor invokes the program data to perform the method of the first aspect.
To solve the above technical problem, a third aspect of the present application provides a computer-readable storage medium having stored thereon program data which, when executed by a processor, implements the method described in the first aspect.
In order to solve the above-mentioned technical problem, a fourth aspect of the present application provides an unmanned aerial vehicle, where an image capturing device capable of capturing an image at a horn of the unmanned aerial vehicle and an unmanned aerial vehicle control device as described in the above-mentioned second aspect are provided.
According to the scheme, the image to be identified, which is acquired by the camera device on the unmanned aerial vehicle, is acquired, the position of the unmanned aerial vehicle arm is determined from the image to be identified, the detection position of the unmanned aerial vehicle arm is obtained, the position of the unmanned aerial vehicle arm in the calibration image is acquired, and the calibration area of the unmanned aerial vehicle arm is determined, wherein the calibration image is an image acquired when the unmanned aerial vehicle arm reaches the unfolding state, so that the calibration area corresponding to the unmanned aerial vehicle arm reaching the unfolding state can be accurately obtained in the calibration image, whether the unmanned aerial vehicle arm reaches the unfolding state is judged based on the position relation between the calibration area and the detection position, the detection result of the unmanned aerial vehicle state is obtained, and therefore whether the unmanned aerial vehicle arm is unfolded in place is analyzed by utilizing the visual characteristics on the image, the influence of signal quality on the detection result is reduced, and the reliability of unmanned aerial vehicle state detection is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a flow chart of an embodiment of a method for detecting a status of an unmanned aerial vehicle according to the present application;
FIG. 2 is a schematic flow chart of another embodiment of a method for detecting a status of a unmanned aerial vehicle according to the present application;
FIG. 3 is a schematic structural view of an embodiment of the unmanned aerial vehicle control device of the present application;
FIG. 4 is a schematic diagram of the architecture of one embodiment of a computer-readable storage medium of the present application;
Fig. 5 is a schematic structural view of an embodiment of the unmanned aerial vehicle of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments, and that adaptive combinations may be made between different embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "system" and "network" are often used interchangeably herein. The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship. Further, "a plurality" herein means two or more than two.
The unmanned aerial vehicle state detection method provided by the application is applied to detecting whether the unmanned aerial vehicle arm reaches the unfolding state, the unmanned aerial vehicle is provided with the camera device capable of collecting images at the unmanned aerial vehicle arm, and the corresponding execution main body is a processing unit capable of calling and processing data collected by the unmanned aerial vehicle.
Referring to fig. 1, fig. 1 is a flow chart of an embodiment of a method for detecting a state of an unmanned aerial vehicle according to the present application, the method includes:
s101: and acquiring an image to be identified acquired by the camera device, and determining the detection position of the unmanned aerial vehicle arm in the image to be identified.
Specifically, an image to be identified, which is acquired by a camera device on the unmanned aerial vehicle, is acquired, the position of the unmanned aerial vehicle arm is determined from the image to be identified, and the detection position of the unmanned aerial vehicle arm is obtained.
In an application mode, an image to be identified, which is acquired by a camera device, is acquired, the outline of the unmanned aerial vehicle arm in the image to be identified is identified, the position corresponding to the outline of the unmanned aerial vehicle arm in the image to be identified is determined, and the detection position of the unmanned aerial vehicle arm is obtained.
In another application mode, an image to be identified, which is acquired by the camera device, is acquired, a positioning area on the unmanned aerial vehicle arm in the image to be identified is identified, and the position corresponding to the positioning area on the unmanned aerial vehicle arm in the image to be identified is determined, so that the detection position of the unmanned aerial vehicle arm is obtained. The positioning area is a selected point location or a divided range on the unmanned aerial vehicle arm.
Optionally, the images to be identified correspond to the same image size, and the images to be identified correspond to a pre-configured image coordinate system, and the detection positions of the unmanned aerial vehicle arm correspond to coordinates of at least part of the area on the unmanned aerial vehicle arm in the image coordinate system.
In some implementation scenes, determining a background area in an image to be identified, extracting image areas outside the background area from the image to be identified, obtaining an image area with higher precision, obtaining image features from the extracted image area, determining the outline of the unmanned aerial vehicle arm based on the image features, and obtaining the coordinates of the outline of the unmanned aerial vehicle arm in the image to be identified.
In some implementation scenes, the positioning area on the unmanned aerial vehicle arm is provided with colors different from other areas, an image area with the colors obviously different is extracted from the image to be identified, the positioning area is determined from the extracted image area, and coordinates of the positioning area in the image to be identified are obtained.
It should be noted that, the image acquisition triggering condition corresponds to the image to be identified acquired by the camera device, and when the image acquisition triggering condition is triggered, the camera device acquires the image to be identified towards the unmanned aerial vehicle horn.
In some implementations, the image acquisition trigger condition is triggered by a user, the position of the unmanned aerial vehicle horn is adjusted by the user, and the image acquisition trigger condition is triggered after the adjustment is completed, so that the image acquisition trigger condition is triggered when the user needs to detect the deployment state of the unmanned aerial vehicle horn.
In some implementation scenarios, the image acquisition triggering condition is automatically triggered, the position of the unmanned aerial vehicle arm is adjusted by the driving device, and the image acquisition triggering condition is triggered after the driving device stops working, so that automation of detecting the unfolding state of the unmanned aerial vehicle arm is realized, and at least one state detection is ensured before the unmanned aerial vehicle executes a task.
It can be appreciated that the imaging device has a higher resolution to ensure detection accuracy, and the imaging device can have the function of acquiring infrared images and thermal imaging images, thereby improving applicability under different environments.
Further, the number of the camera devices is at least one, when the number of the camera devices is one, the camera devices adopt wide-angle cameras, so that all unmanned aerial vehicle arms are included in the image to be identified, and when the number of the camera devices is multiple, at least one unmanned aerial vehicle arm is included in the image to be identified, which is acquired by the camera devices. When each unmanned aerial vehicle arm corresponds to one camera device, the camera device faces the corresponding unmanned aerial vehicle arm and collects images to be identified.
S102: acquiring a calibration area of the unmanned aerial vehicle arm in a calibration image, and determining whether the unmanned aerial vehicle arm reaches an unfolding state or not based on the calibration area and the detection position; the calibration image is an image acquired when the unmanned aerial vehicle arm reaches an unfolding state.
Specifically, the position of the unmanned aerial vehicle arm in the calibration image is obtained, the calibration area of the unmanned aerial vehicle arm is determined, wherein the calibration image is an image acquired when the unmanned aerial vehicle arm reaches the unfolding state, so that the calibration area corresponding to the unmanned aerial vehicle arm reaching the unfolding state can be accurately obtained in the calibration image, and whether the unmanned aerial vehicle arm reaches the unfolding state or not is judged based on the position relation between the calibration area and the detection position, and the detection result of the unmanned aerial vehicle state is obtained.
It can be understood that the calibration image is an image of the camera device collecting the corresponding unmanned aerial vehicle arm in the unfolded state, the calibration area is predetermined and the determination process of the calibration area is similar to the determination process of the detection position, but considering the collection error and the environmental change, the calibration area is determined based on a plurality of calibration images, the calibration area represents the theoretical range of at least part of the area on the unmanned aerial vehicle arm in the calibration image, that is, when the calibration area is arranged in any calibration image, the unmanned aerial vehicle arm in the unfolded state is positioned in the calibration area.
In an application mode, the detection position and the calibration area are determined based on the outline of the unmanned aerial vehicle arm, the coincidence degree of the detection position and the calibration area is obtained, and whether the unmanned aerial vehicle arm reaches the unfolding state is determined based on the coincidence degree. When the detection position is completely located in the calibration area, the unmanned aerial vehicle arm is judged to reach the unfolding state, and when the detection position is not completely located in the calibration area, the unmanned aerial vehicle arm is judged to not reach the unfolding state.
In another application mode, the detection position and the calibration area correspond to positioning areas on the unmanned aerial vehicle arm, the distance difference between the respective center points of the detection position and the calibration area is acquired for each positioning area, and whether the unmanned aerial vehicle arm reaches the unfolding state is determined based on the distance difference. When the distance difference is smaller than the distance threshold, the unmanned aerial vehicle arm is judged to reach the unfolding state, and when the distance difference is larger than or equal to the distance threshold, the unmanned aerial vehicle arm is judged to not reach the unfolding state.
It can be understood that the accurate position of the unmanned aerial vehicle arm is calibrated and detected by utilizing the visual features on the images, whether the unmanned aerial vehicle arm is unfolded in place or not is analyzed, the influence of signal quality on a detection result is reduced, and the reliability of unmanned aerial vehicle state detection is improved. When the plurality of camera devices acquire at least one corresponding unmanned aerial vehicle arm respectively, the unmanned aerial vehicle arm in the image to be identified acquired by each camera device is detected based on the steps.
Optionally, after determining whether the unmanned aerial vehicle arm reaches the detection result of the unfolding state, if the unmanned aerial vehicle arm does not reach the unfolding state, generating prompt information and displaying the prompt information to the user so as to prompt the user to adjust the unmanned aerial vehicle arm to the unfolding state, and detecting again after the user adjusts. The prompt information can be subjected to voice broadcasting and can also be sent to an application program bound with the unmanned aerial vehicle.
According to the scheme, the image to be identified, which is acquired by the camera device on the unmanned aerial vehicle, is acquired, the position of the unmanned aerial vehicle arm is determined from the image to be identified, the detection position of the unmanned aerial vehicle arm is obtained, the position of the unmanned aerial vehicle arm in the calibration image is acquired, and the calibration area of the unmanned aerial vehicle arm is determined, wherein the calibration image is an image acquired when the unmanned aerial vehicle arm reaches the unfolding state, so that the calibration area corresponding to the unmanned aerial vehicle arm reaching the unfolding state can be accurately obtained in the calibration image, whether the unmanned aerial vehicle arm reaches the unfolding state is judged based on the position relation between the calibration area and the detection position, the detection result of the unmanned aerial vehicle state is obtained, and therefore whether the unmanned aerial vehicle arm is unfolded in place is analyzed by utilizing the visual characteristics on the image, the influence of signal quality on the detection result is reduced, and the reliability of unmanned aerial vehicle state detection is improved.
Referring to fig. 2, fig. 2 is a schematic flow chart of another embodiment of a method for detecting a state of an unmanned aerial vehicle according to the present application, wherein a driving motor is connected to an end of an unmanned aerial vehicle arm far away from a base body of the unmanned aerial vehicle, and a locking mechanism is provided on the unmanned aerial vehicle arm, and the method includes:
s201: and acquiring an image to be identified acquired by the camera device, and determining the detection position of the unmanned aerial vehicle arm in the image to be identified.
Specifically, an image to be identified acquired by a camera device is acquired, a detection position of a locking mechanism on an unmanned aerial vehicle arm in the image to be identified is determined, and a detection position of a driving motor connected with the unmanned aerial vehicle arm is determined.
In an application mode, an image to be identified acquired by the camera device is acquired, and detection coordinates of a driving motor and a locking mechanism in the image to be identified are determined.
Specifically, an image to be identified, which is acquired by the camera device, is acquired, a driving motor and a locking mechanism in the image to be identified are detected, a detection frame where the driving motor and the locking mechanism are located is obtained, and detection coordinates corresponding to the detection frame where the driving motor and the locking mechanism are located are determined in the image to be identified. The image to be identified and the calibration image are corresponding to the same image size, the image to be identified and the calibration image are provided with the same image coordinate system, the detection coordinate of the driving motor is the coordinate of the detection frame of the driving motor in the image coordinate system, and the detection coordinate of the locking mechanism is the coordinate of the detection frame of the locking mechanism in the image coordinate system.
It can be appreciated that the driving motor and the locking mechanism have smaller dimensions than the whole unmanned aerial vehicle arm, and detection of the driving motor and the locking mechanism in the image to be identified can be disassembled into detection of the driving motor and the locking mechanism, and the smaller-dimension area is extracted from the unmanned aerial vehicle arm and the area connected with the unmanned aerial vehicle arm, so that the detection process is finer, and the detection precision is improved.
It should be noted that, in addition to the driving motor and the locking mechanism described in the present application, other areas related to the unmanned aerial vehicle arm may be used as the detected area, which is not particularly limited in the present application.
In some implementations, each unmanned aerial vehicle arm of the unmanned aerial vehicle further includes a sensor connected to the locking mechanism, and when the locking mechanism is locked, the corresponding sensor triggers a locking signal.
It should be noted that, before obtaining the image to be identified that the camera device gathered and determining the detection position of the unmanned aerial vehicle arm in the image to be identified, the method further includes: and sending an image acquisition instruction to the camera device in response to the locking signal triggered by the sensor on the at least one unmanned aerial vehicle arm.
Specifically, after detecting a sensor trigger locking signal on at least one unmanned aerial vehicle horn, an image acquisition instruction is sent to the camera device so that the camera device acquires an image to be identified.
It can be understood that after the locking mechanism locks the unmanned aerial vehicle horn, the unmanned aerial vehicle horn can not continue to be adjusted, the corresponding sensor detects that the locking mechanism is locked and then triggers the locking signal, when the locking signal triggered by the sensor on at least one unmanned aerial vehicle horn is obtained, an image acquisition instruction is sent to the corresponding camera device, so that the automation of image acquisition to be identified is realized, the image to be identified is acquired after the unmanned aerial vehicle horn can not be adjusted, and the accuracy of the acquisition time node of the image to be identified is improved.
In a specific implementation scenario, each camera device corresponds an unmanned aerial vehicle horn, after locking mechanism on the unmanned aerial vehicle horn has locked, sends image acquisition instruction to corresponding camera device to gather the time node of waiting to discern the image to each unmanned aerial vehicle horn and carry out accurate control, and detect whether each unmanned aerial vehicle horn reaches the expansion state respectively.
In a specific implementation scene, each camera device corresponds to an unmanned aerial vehicle horn, after a locking mechanism on any unmanned aerial vehicle horn is locked, an image acquisition instruction is sent to all camera devices, so that images to be identified at all unmanned aerial vehicle horn positions are acquired, and whether all unmanned aerial vehicle horns acquired by the camera devices reach an unfolding state is detected in time. When part of unmanned aerial vehicle arms do not reach the unfolding state, prompt information for adjusting the unmanned aerial vehicle arms which do not reach the unfolding state is generated, and therefore instantaneity of the prompt information is improved.
In a specific implementation scenario, each camera device corresponds to a plurality of unmanned aerial vehicle arms, and after one locking mechanism in the plurality of unmanned aerial vehicle arms corresponding to the camera device is locked, an image acquisition instruction is sent to the corresponding camera device, so that whether all unmanned aerial vehicle arms acquired by the camera device reach an unfolding state or not is detected in time. When part of unmanned aerial vehicle arms do not reach the unfolding state, prompt information for adjusting the unmanned aerial vehicle arms which do not reach the unfolding state is generated, and therefore instantaneity of the prompt information is improved.
Optionally, the driving motor and the locking mechanism each comprise at least one calibration range, and colors different from other areas are arranged in the calibration ranges.
Specifically, at least one calibration range is preset on each of the driving motor and the locking mechanism, and colors different from other areas are set in the calibration ranges, so that difficulty in positioning the calibration ranges in the image to be identified is reduced, and processing resource consumption in image processing is saved.
In a specific implementation scenario, the driving motor comprises a motor base and a motor body, the driving motor is divided into respective corresponding calibration ranges according to the motor base and the motor body in advance, and the locking mechanism corresponds to one calibration range. The shape corresponding to the calibration range may be any custom shape, which is not particularly limited in the present application.
S202: acquiring a calibration area of the unmanned aerial vehicle arm in a calibration image, and determining whether the unmanned aerial vehicle arm reaches an unfolding state or not based on the calibration area and the detection position; the calibration image is an image acquired when the unmanned aerial vehicle arm reaches an unfolding state.
Specifically, a calibration area of the unmanned aerial vehicle arm in a calibration image is obtained, and whether the unmanned aerial vehicle arm reaches an unfolding state or not is judged based on the position relation between the calibration area and the detection position, so that a detection result of the unmanned aerial vehicle state is obtained.
It can be appreciated that the calibration area corresponds to a calibration coordinate range of the driving motor and the locking mechanism, and determining whether the unmanned aerial vehicle arm reaches the unfolded state based on the calibration area and the detection position includes: and determining whether the unmanned aerial vehicle arm reaches the unfolding state or not based on the calibration coordinate range of the driving motor and the detection coordinate of the driving motor and the calibration coordinate range of the locking mechanism and the detection coordinate of the locking mechanism.
Specifically, based on the position relation between the calibration coordinate range of the driving motor and the detection coordinate of the driving motor and the position relation between the calibration coordinate range of the locking mechanism and the detection coordinate of the locking mechanism, whether the unmanned aerial vehicle arm reaches the unfolding state is determined, so that the precision of state detection is improved in a finer coordinate matching mode in a small-size area.
In a specific implementation scenario, a first overlap ratio between a calibration coordinate range of the driving motor and a detection coordinate of the driving motor and a second overlap ratio between the calibration coordinate range of the locking mechanism and the detection coordinate of the locking mechanism are obtained, and whether the unmanned aerial vehicle arm reaches an unfolding state is determined based on the first overlap ratio and the second overlap ratio. Only when the detection coordinates of the driving motor and the locking mechanism are in the corresponding calibration coordinate ranges, the unmanned aerial vehicle arm is judged to reach the unfolding state.
In a specific implementation scenario, a first distance difference between a center point of a calibration coordinate range of the driving motor and a detection coordinate of the driving motor is obtained, and a second distance difference between the center point of the calibration coordinate range of the locking mechanism and the detection coordinate of the locking mechanism is obtained, and whether the unmanned aerial vehicle arm reaches an unfolding state is determined based on the first distance difference and the second distance difference. And judging that the unmanned aerial vehicle arm reaches the unfolding state only when the first distance difference and the second distance difference are smaller than the distance threshold value.
It should be noted that, the calibration process for calibrating the coordinate range includes: acquiring a plurality of calibration images with the same size, and determining a center coordinate in the calibration images; and determining the calibration coordinate range of the driving motor based on the calibration range and the center coordinates of the driving motor in the plurality of calibration images, and determining the calibration coordinate range of the locking mechanism based on the calibration range and the center coordinates of the locking mechanism in the plurality of calibration images.
Specifically, a plurality of calibration images are obtained and preprocessed to obtain calibration images with the same size, and center coordinates are determined in the calibration images with the same size. Wherein the center coordinates may be specified locations in the calibration image.
Further, based on the distance between the calibration range of the driving motor in the plurality of calibration images and the center coordinate, the theoretically corresponding calibration coordinate range of the driving motor is defined in the calibration images, so that the influence of the acquisition angle and the environmental change on the calibration coordinate range of the driving motor is reduced. Similarly, based on the distance between the calibration range of the locking mechanism in the plurality of calibration images and the center coordinate, the theoretically corresponding calibration coordinate range of the locking mechanism is defined from the calibration images, so that the influence of the acquisition angle and the environmental change on the calibration coordinate range of the locking mechanism is reduced.
S203: and responding Ren Yimo that the unmanned aerial vehicle arm does not reach the unfolding state, and acquiring the task state of the unmanned aerial vehicle.
Specifically, when any unmanned aerial vehicle arm is detected not to reach the unfolding state, the current task state of the unmanned aerial vehicle is obtained, so that whether the unmanned aerial vehicle is currently executing a task or not is confirmed.
S204: based on the task state, a hint information is generated relating to the unmanned aerial vehicle arm that did not reach the deployed state.
Specifically, when the task state is the non-takeoff state, the prompt information is used for prompting the user to check the unmanned aerial vehicle arm which does not reach the unfolding state, and when the task state is the takeoff state, the prompt information is used for prompting the user to control the unmanned aerial vehicle to land and check the unmanned aerial vehicle arm which does not reach the unfolding state. Therefore, prompt information is utilized to timely send out prompt to a user, and the safety of the unmanned aerial vehicle for executing tasks is improved.
It can be appreciated that when the unmanned aerial vehicle does not take off to perform a task, a prompt message for prompting the user to check the unmanned aerial vehicle arm which does not reach the unfolded state is generated. When the unmanned aerial vehicle takes off to execute tasks, generating prompt information for prompting a user to control the unmanned aerial vehicle to land as soon as possible and checking the unmanned aerial vehicle arm which does not reach the unfolding state after landing.
Optionally, the prompt information includes the position of the unmanned aerial vehicle arm which does not reach the unfolding state, so that the user can position the unmanned aerial vehicle arm which needs to be adjusted as soon as possible. In addition, when the task state is the take-off state, the output power of the driving motor is limited, so that the probability of the unmanned aerial vehicle arm folding back in the flight process caused by the fact that the unmanned aerial vehicle arm does not reach the unfolding state is reduced.
In this embodiment, keep away from the one end of unmanned aerial vehicle base member on the unmanned aerial vehicle horn and be connected with driving motor, and be provided with locking mechanism on the unmanned aerial vehicle horn, through the detection of driving motor and locking mechanism in treating the discernment image, can disassemble the detection to whole unmanned aerial vehicle horn into the detection to driving motor and locking mechanism, draw less size's region from on the unmanned aerial vehicle horn and from the unmanned aerial vehicle horn in the region that is connected, make detection process more meticulous, based on driving motor's demarcation coordinate range and driving motor's the positional relationship between the detection coordinates, and locking mechanism's demarcation coordinate range and the positional relationship between locking mechanism's the detection coordinates, confirm whether the unmanned aerial vehicle horn reaches the expansion state, thereby through more meticulous coordinate matching mode in the region of small-size, improve the precision that the state detected, when detecting any unmanned aerial vehicle horn does not reach the expansion state, utilize prompt information to in time send the warning to the user, improve unmanned aerial vehicle and carry out the security of task.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an embodiment of a drone control device according to the present application, the drone control device 30 includes a memory 301 and a processor 302 coupled to each other, wherein the memory 301 stores program data (not shown), and the processor 302 invokes the program data to implement the method for detecting the status of the drone in the above embodiment, and the description of the related content is referred to the detailed description of the above method embodiment and will not be repeated here.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of a computer readable storage medium 40 according to the present application, where the computer readable storage medium 40 stores program data 400, and the program data 400, when executed by a processor, implements the method for detecting the status of the unmanned aerial vehicle in the above embodiment, and details of the related content are described in the above method embodiment and are not repeated herein.
Referring to fig. 3 and 5, fig. 5 is a schematic structural diagram of an embodiment of the unmanned aerial vehicle of the present application, and an image capturing device 500 capable of capturing images at the arm of the unmanned aerial vehicle and the unmanned aerial vehicle control device 30 described in the above embodiment are disposed on the unmanned aerial vehicle 50.
The units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (10)
1. An unmanned aerial vehicle state detection method is characterized in that an image pickup device capable of collecting images at an unmanned aerial vehicle arm is arranged on an unmanned aerial vehicle, and the method comprises the following steps:
Acquiring an image to be identified acquired by the camera device, and determining the detection position of the unmanned aerial vehicle arm in the image to be identified;
Acquiring a calibration area of the unmanned aerial vehicle arm in a calibration image, and determining whether the unmanned aerial vehicle arm reaches an unfolding state or not based on the calibration area and the detection position; the calibration image is an image acquired when the unmanned aerial vehicle arm reaches the unfolding state.
2. The unmanned aerial vehicle state detection method according to claim 1, wherein one end, far away from an unmanned aerial vehicle matrix, of the unmanned aerial vehicle arm is connected with a driving motor, and a locking mechanism is arranged on the unmanned aerial vehicle arm;
The obtaining the image to be identified acquired by the camera device, and determining the detection position of the unmanned aerial vehicle arm in the image to be identified comprises the following steps:
and acquiring an image to be identified acquired by the camera device, and determining detection coordinates of the driving motor and the locking mechanism in the image to be identified.
3. The unmanned aerial vehicle state detection method of claim 2, wherein the calibration area corresponds to a calibrated coordinate range of the drive motor and the locking mechanism;
Based on the calibration area and the detection position, determining whether the unmanned aerial vehicle horn reaches a deployed state includes:
And determining whether the unmanned aerial vehicle arm reaches an unfolding state or not based on the calibration coordinate range of the driving motor and the detection coordinate of the driving motor, and the calibration coordinate range of the locking mechanism and the detection coordinate of the locking mechanism.
4. A method of unmanned aerial vehicle condition detection according to claim 3, wherein the calibration process for calibrating the coordinate range comprises:
acquiring a plurality of calibration images with the same size, and determining a center coordinate in the calibration images;
and determining a calibration coordinate range of the driving motor based on the calibration range and the center coordinates of the driving motor in the calibration images, and determining the calibration coordinate range of the locking mechanism based on the calibration range and the center coordinates of the locking mechanism in the calibration images.
5. The unmanned aerial vehicle state detection method of claim 2, wherein each of the unmanned aerial vehicle arms of the unmanned aerial vehicle further comprises a sensor connected to the locking mechanism, and the corresponding sensor triggers a locking signal when the locking mechanism is locked;
the method for acquiring the image to be identified acquired by the camera device, before determining the detection position of the unmanned aerial vehicle arm in the image to be identified, further comprises:
and responding to the locking signal triggered by the sensor on at least one unmanned aerial vehicle arm, and sending an image acquisition instruction to the camera device.
6. The unmanned aerial vehicle state detection method of claim 2, wherein the driving motor and the locking mechanism each comprise at least one calibration range, and wherein colors different from other areas are arranged in the calibration ranges.
7. The method for detecting the state of the unmanned aerial vehicle according to claim 1, wherein the step of acquiring the calibration area of the unmanned aerial vehicle arm in the calibration image, and determining whether the unmanned aerial vehicle arm reaches the deployed state based on the calibration area and the detection position, further comprises:
Acquiring a task state of the unmanned aerial vehicle in response to any unmanned aerial vehicle arm not reaching a deployment state;
Generating prompt information related to the unmanned aerial vehicle arm which does not reach the unfolding state based on the task state; when the task state is the non-takeoff state, the prompt information is used for prompting a user to check the unmanned aerial vehicle arm which does not reach the unfolding state, and when the task state is the takeoff state, the prompt information is used for prompting the user to control the unmanned aerial vehicle to drop and check the unmanned aerial vehicle arm which does not reach the unfolding state.
8. An unmanned aerial vehicle control device, characterized by comprising: a memory and a processor coupled to each other, wherein the memory stores program data that the processor invokes to perform the method of any of claims 1-7.
9. A computer readable storage medium having stored thereon program data, which when executed by a processor implements the method of any of claims 1-7.
10. An unmanned aerial vehicle, wherein the unmanned aerial vehicle is provided with a camera device capable of collecting images at the unmanned aerial vehicle arm and the unmanned aerial vehicle control device according to claim 8.
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