Airport clearance zone obstacle checking method, device and equipment and storage medium

Abstract

The embodiment of the invention provides an airport clearance zone obstacle checking method, device and equipment and a storage medium, belongs to the technical field of airport safety monitoring, and can be applied to the field of low-altitude economy. The method comprises the following steps: acquiring a shielding parameter of the airport surveillance radar; according to the shielding parameter, a holder is controlled to move to a target attitude, the setting position of the holder is determined based on the setting position of the airport surveillance radar, and the holder is used for carrying a shooting device; controlling the holder to stop moving so as to shoot through the shooting device to obtain a target image; segmenting the target image through a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; and carrying out clearance area obstacle checking on the foreground pixel point set. According to the embodiment of the invention, the cost of airport clearance zone obstacle investigation can be reduced, and the accuracy, simplicity and automation degree of airport clearance zone obstacle investigation are improved.

Description

Technical Field

[0001] The present application relates to the technical field of airport safety monitoring and the field of low-altitude economy, and in particular to an airport clearance zone obstacle detection method, device, equipment and storage medium. Background Art

[0002] Airport clear zone refers to the airspace and area specified to ensure that aircraft are not disturbed by obstacles (such as buildings, towers, hills, trees, etc.) during takeoff, landing and other flight operations. In the airport clear zone, obstacles cannot exceed a certain height.

[0003] At present, the inspection of obstacles in the airport airspace in the aviation field generally adopts the method of manual inspection of obstacles, which is time-consuming and labor-intensive and cannot meet the real-time inspection needs.

[0004] In related technologies, laser radar is also used to perform spatial scanning of the airport clear area in order to collect spatial data (such as point cloud data) for complex three-dimensional modeling and to check for possible obstacles. However, due to the short scanning distance of laser radar, it is necessary to use drones or special vehicles to carry laser radar for scanning, which is expensive and time-consuming. It depends on manual operation to plan and deploy laser radar scanning, and any updates or changes require manual re-planning of new scanning tasks and re-modeling, and each modeling also consumes a lot of computing resources.

[0005] In related technologies, there is also the use of multi-camera stereo vision systems to collect three-dimensional images or depth images of the airport clear area for analysis to check for possible obstacles. However, multi-camera stereo vision systems require the coordinated work of multiple cameras, which not only cannot cover long distances and affect the accuracy of inspection, but also involve complex installation, synchronization, calibration, image processing and data fusion, and maintenance work is also cumbersome.

[0006] Therefore, how to reduce the cost of obstacle inspection in airport clear zone and improve the accuracy, simplicity and automation of obstacle inspection in airport clear zone has become a technical problem that needs to be solved urgently. Summary of the invention

[0007] The main purpose of the embodiments of the present application is to propose a method, device, equipment and storage medium for checking obstacles in the airport clearance zone, aiming to reduce the cost of checking obstacles in the airport clearance zone and improve the accuracy, simplicity and automation of checking obstacles in the airport clearance zone.

[0008] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a method for checking obstacles in an airport clearance zone, the method comprising: Obtain the shielding parameters of the airport surveillance radar; According to the shielding parameter, control the gimbal to move to a target posture, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; Controlling the pan / tilt head to stop moving so as to shoot with the shooting device to obtain a target image; Segmenting the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; The foreground pixel set is checked for obstacles in the clear area.

[0009] To achieve the above-mentioned purpose, a second aspect of an embodiment of the present application provides an airport clearance zone obstacle detection device, the device comprising: A parameter acquisition module is used to obtain the shielding parameters of the airport surveillance radar; A gimbal control module, used for controlling the gimbal to move to a target posture according to the shielding parameter, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; An image shooting module, used for controlling the pan / tilt head to stop moving so as to shoot through the shooting device to obtain a target image; An image segmentation module is used to segment the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; The obstacle detection module is used to detect obstacles in the clear area of ​​the foreground pixel set.

[0010] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes a computer device, which includes a memory and a processor, the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0011] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0012] In the scheme implemented by the airport clear zone obstacle inspection method, device, equipment and storage medium proposed in the present application, the shielding parameters of the airport surveillance radar are obtained; according to the shielding parameters, the gimbal is controlled to move to the target posture, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; the gimbal is controlled to stop moving so as to shoot through the shooting device to obtain a target image; the target image is segmented by a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; and the foreground pixel point set is inspected for obstacles in the clear zone. In this way, on the one hand, through the shielding parameters of the airport surveillance radar, the gimbal is reasonably guided to move accurately to the target posture, so that the shooting device carried by the gimbal can capture the target image that can cover the area that may be blocked by obstacles in real time; on the other hand, through the pre-trained image segmentation model, the foreground pixel point set is accurately segmented from the target image, which can effectively eliminate irrelevant interference factors in the target image, so that only the foreground pixel point set can be focused on obstacle inspection, thereby improving the efficiency and accuracy of obstacle inspection in the clear area. Therefore, the intelligent and automated obstacle inspection in the airport clear area is realized through gimbal control, thereby reducing the cost of obstacle inspection in the airport clear area and improving the accuracy, simplicity and automation of obstacle inspection in the airport clear area. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of the airport clearance zone obstacle detection method provided by the embodiment of the present application; Figure 2 This is an example diagram of an application scenario of obstacle inspection in an airport clearance zone provided by an embodiment of the present application; Figure 3 is an example diagram of a target image provided by an embodiment of the present application; Figure 4 is a schematic block diagram of an airport clearance zone obstacle detection device provided in an embodiment of the present application; Figure 5 It is a schematic block diagram of the structure of the computer device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0015] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0017] First, the nouns involved in this application are analyzed: Airport Surveillance Radar (ASR): refers to a radar system used to monitor the position of aircraft within the airport and the airspace surrounding the airport. The main function of the airport surveillance radar is to provide real-time monitoring and tracking of aircraft within the airport's clear airspace to ensure the safety of aircraft during takeoff and landing.

[0018] Obstruction parameter: refers to the factors that affect the signal propagation or coverage of the airport surveillance radar, which can be used to quantify or evaluate whether certain areas will not be effectively monitored by the airport surveillance radar. It reflects the situation where the airport surveillance radar's surveillance capability is limited due to the impact of obstacles on the signal propagation of the airport surveillance radar.

[0019] Shielding angle: also known as blind spot angle or blocking angle, refers to the specific angle range where obstacles around the airport (such as buildings, towers, hills, trees, etc.) cause the airport surveillance radar to be unable to effectively monitor.

[0020] Target shielding angle: also known as the limiting angle of shielding angle, refers to the upper threshold angle of shielding angle caused by obstacles around the airport to the airport surveillance radar. In other words, the shielding angle caused by obstacles around the airport to the airport surveillance radar must not exceed the target shielding angle. Once the target shielding angle is exceeded, the airport surveillance radar will have a blind spot, affecting the airport surveillance radar's monitoring of aircraft.

[0021] The shielding parameters include the target shielding angle.

[0022] Gimbal: refers to a gimbal system that can carry a camera or an integrated gimbal camera that carries a camera. The gimbal carries a camera and is used to fix the camera and change the height, pitch angle or direction of the camera to keep the camera stable in a certain posture for easy shooting.

[0023] The shooting device can be a monocular camera. Compared with laser radar and multi-eye stereo vision systems, the pan / tilt system equipped with a monocular camera has obvious advantages of low cost, small size, easy maintenance, simplicity, flexibility and stability, and can quickly capture stable images of different angles of the airport surrounding environment at any time.

[0024] YOLOv8 Segmentation Network: It is an efficient and advanced deep network model in the field of object detection. It is a new version of the YOLO (You Only Look Once) series of models. The YOLOv8 Segmentation Network inherits the advantages of YOLOv8 and is optimized to classify images at the pixel level, thereby achieving more refined image understanding.

[0025] Based on this, the embodiments of the present application provide a method, device, equipment and storage medium for checking obstacles in the airport clearance zone, aiming to reduce the cost of checking obstacles in the airport clearance zone and improve the accuracy, simplicity and automation of checking obstacles in the airport clearance zone.

[0026] The airport clear zone obstacle detection method, device, equipment and storage medium provided in the embodiments of the present application are specifically described through the following embodiments. First, the airport clear zone obstacle detection method in the embodiments of the present application is described.

[0027] The airport clearance zone obstacle inspection method provided in the embodiment of the present application relates to the field of airport safety monitoring technology and the field of low-altitude economy. The airport clearance zone obstacle inspection method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the airport clearance zone obstacle inspection method, such as airport clearance zone obstacle inspection software, but is not limited to the above forms. Among them, the terminal or server communicates with the pan-tilt head through the network.

[0028] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0029] Figure 1 is an optional flow chart of the airport clearance zone obstacle detection method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S105.

[0030] Step S101, obtaining shielding parameters of the airport surveillance radar.

[0031] In order to facilitate the rapid and automated inspection of obstacles in the airport's clear area, the shielding parameters of the airport surveillance radar are obtained as a basis for inspection.

[0032] Step S102, controlling the gimbal to move to a target posture according to the shielding parameters, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device.

[0033] By setting the PTZ at an appropriate position, the shooting device can shoot obstacles whose shielding angle to the airport monitoring radar is greater than the target shielding angle. The setting position of the PTZ is determined by the setting position of the airport monitoring radar.

[0034] For example, taking the pan-tilt camera as an example, the airport surveillance radar is located on the radar tower. Specifically, the pan-tilt camera can be installed at the base of the radar tower. The installation position is level or at the same height as the bottom of the airport surveillance radar. The installation is simple and easy to maintain, and the occupied area is also small.

[0035] In this way, based on the shielding parameters of the airport surveillance radar, the gimbal can be reasonably guided to move precisely to the target posture, so that the shooting device can take high-precision photos of areas that may be blocked by obstacles.

[0036] Before step S102 in some embodiments, the zero-degree position of the gimbal may be calibrated, and the shooting angle of the shooting device may be calibrated.

[0037] In order to ensure that the image captured by the camera is stable and accurate, after the gimbal is installed, the zero-degree position of the gimbal can be calibrated, that is, the initial position of the gimbal is set to zero degrees, and the installation angle of the camera is checked and adjusted by a level to calibrate the shooting angle of the camera so that the visual center line of the camera is aligned with the zero-degree line of the level to ensure that the lens of the camera is parallel to the horizontal plane. In this way, the zero-degree position of the gimbal is aligned with the zero-degree horizontal viewing angle of the camera, that is, when the gimbal is at zero degrees, its rotation axis and the visual axis of the camera are parallel to the horizontal plane, and the shooting angle of the camera is exactly horizontal.

[0038] In some embodiments, in order to improve the accuracy and simplicity of obstacle detection in the airport clearance zone, the shielding parameter may be a target shielding angle. According to relevant flight safety regulations, the shielding angle of obstacles to airport radars shall not exceed 0.25 degrees, so the target shielding angle may be 0.25 degrees. In step S102 of some embodiments, the gimbal may be controlled to rotate at a target motion speed until the pitch angle of the gimbal reaches a target shielding angle.

[0039] Specifically, the target posture may be that the pitch angle of the gimbal is equal to the target shielding angle.

[0040] The gimbal can have multiple motion modes. In order to avoid the error of the gimbal moving to the target posture, the gimbal is controlled to rotate at the target motion speed until the pitch angle of the gimbal is the target shielding angle.

[0041] The target motion speed may be a fixed motion speed. In order to precisely adjust the pitch angle of the gimbal to the target shielding angle, the target motion speed may be a relatively small fixed motion speed. For example, the gimbal is controlled to rotate at a relatively small fixed motion speed of 0.01 degrees / s with a zero-degree position as the starting position until the pitch angle of the gimbal reaches 0.25 degrees.

[0042] In this way, by performing extremely delicate and slow motion control on the pan / tilt head, it is convenient to accurately adjust the pitch angle of the pan / tilt head to the target shielding angle.

[0043] Step S103, controlling the pan / tilt head to stop moving so as to shoot through the shooting device to obtain a target image.

[0044] When the pitch angle of the gimbal is adjusted to the target shielding angle, the gimbal is controlled to stop moving, and the shooting device is controlled to shoot to obtain the target image, so as to quickly check for obstacles in the clear area based on the target image.

[0045] It should be noted that when the gimbal is in the target posture, the target image captured by the shooting device can cover the screen of obstacles whose shielding angle to the airport monitoring radar is greater than the target shielding angle.

[0046] Step S104, segmenting the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image.

[0047] The target image can be segmented using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image, thereby improving the segmentation accuracy of the foreground pixel point set.

[0048] Exemplarily, the pre-trained image segmentation model may be a pre-trained YOLOv8Segmentation network.

[0049] Before step S104 in some embodiments, an untrained image segmentation model may be trained.

[0050] Exemplarily, the untrained image segmentation model may be a YOLOv8 Segmentation network. Specifically, the YOLOv8 Segmentation network may be iteratively trained using a preset image sample and its actual pixel-level label of the foreground, so as to effectively learn the predicted foreground pixel points of the image sample, wherein the image sample includes different foreground objects (such as trees, buildings, etc.) of the airport environment, and the actual pixel-level label of the foreground refers to the pixel points of the foreground object marked for the image sample by a mask; thereby calculating the loss between the actual pixel-level label of the foreground and the predicted foreground pixel points, calculating the loss function of the YOLOv8 Segmentation network based on the loss, and then updating the parameters of the YOLOv8 Segmentation network based on the loss function, until the number of iterations is greater than the preset number threshold, stopping the training of the YOLOv8 Segmentation network, and obtaining a trained YOLOv8 Segmentation network. The trained YOLOv8 Segmentation network can accurately segment the foreground pixel set in the complex background of the airport environment.

[0051] In this way, by segmenting the foreground pixel set from the target image, irrelevant interference factors in the target image can be effectively eliminated, and the subsequent obstacle detection will become simpler and clearer. Compared with obstacle detection for the entire target image, the amount of calculation is greatly reduced, thereby greatly reducing the computational complexity of obstacle detection, and can save computing resources while improving the detection speed.

[0052] Step S105, checking the clear area obstacles of the foreground pixel set.

[0053] The foreground pixel set contains the main information of the obstacle. Only the foreground pixel set is used to more focus on the obstacle detection in the clear area. This can ignore irrelevant background information, thereby providing more accurate and rapid obstacle detection and improving the efficiency and accuracy of obstacle detection.

[0054] In step S105 of some embodiments, centerline detection processing may be performed on the target image to determine the horizontal centerline of the target image; determine whether there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal centerline; if there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal centerline, determine that there are obstacles that damage the airport's clear zone; if there are no foreground pixel points in the foreground pixel point set whose height exceeds the horizontal centerline, determine that there are no obstacles that damage the airport's clear zone.

[0055] Since the pitch angle of the gimbal is the target shielding angle of the airport surveillance radar, it means that the horizontal center line of the visual imaging of the shooting device coincides with the boundary of the target shielding angle. The boundary of the target shielding angle refers to the ray emitted from the setting position of the gimbal and having an angle with the horizontal reference line as the target shielding angle. At this time, objects above the horizontal center line of the visual imaging exceed the boundary of the target shielding angle, that is, exceed the allowable range of the shielding angle of the airport surveillance radar, and are obstacles that destroy the airport clear zone.

[0056] Based on this, a centerline detection process is performed on the target image to determine the horizontal centerline of the target image, which is the axis of symmetry of the target image in the horizontal direction. Then, it is detected whether there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal centerline. If there are no foreground pixel points in the foreground pixel point set whose height exceeds the horizontal centerline, it is determined that there are no obstacles that damage the airport's clear zone. If there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal centerline, the foreground pixel points whose height exceeds the horizontal centerline are determined to be obstacles that damage the airport's clear zone.

[0057] For a better understanding of the above embodiments, please refer to Figure 2 , Figure 2 This is an example of an application scenario for obstacle inspection in the airport clearance area. Figure 2In the diagram, taking the scene of trees around the airport as an example, the PTZ camera is installed at the base of the radar tower. The installation position is horizontal or at the same height as the bottom of the airport surveillance radar. The allowable range of the shielding angle of the airport surveillance radar is <0.25 degrees, that is, the target shielding angle is 0.25 degrees. Then, when the pitch angle of the PTZ camera is 0.25 degrees, the horizontal center line of the visual imaging of the PTZ camera coincides with the 0.25-degree ray. Trees that are higher than the horizontal center line of the visual imaging of the PTZ camera will exceed the 0.25-degree ray, which is an obstacle that destroys the airport clear zone. At this time, the target image captured by the PTZ camera is as follows: Figure 3 As shown, in Figure 3 In the illustrated target image, trees are foreground objects and the background is the airspace. The target image is segmented by a pre-trained image segmentation model to distinguish the pixel point set {F} of the trees from the pixel point set {B} of the background airspace. The target image is subjected to centerline detection to determine the horizontal centerline of the target image. The pixel point set {F} of the trees is segmented using the horizontal centerline as the dividing line to obtain pixel points {F+} above the horizontal centerline and pixel points {F-} below the horizontal centerline in the pixel point set {F} of the trees. Pixel points {F+} are the pixel points of the trees in the forest that are beyond the ray of 0.25 degrees, indicating obstacles that damage the airport clear zone. Pixel points {F-} are the pixel points of the safe trees in the forest that will not damage the airport clear zone.

[0058] In this way, by deploying the gimbal equipped with a shooting device at an appropriate specific position and adjusting the pitch angle of the gimbal to the target shielding angle of the airport surveillance radar, the shooting device can capture the target image of a specific perspective in real time, and through the trained image segmentation model, the foreground pixel point set of the target image is segmented, so that the foreground pixel point set can be quickly and accurately checked for obstacles directly based on the horizontal center line of the target image, thereby providing a simpler and more accurate obstacle detection while reducing the detection cost.

[0059] When obstacles that damage the airport's clear zone are detected, foreground pixels whose heights exceed the horizontal center line can be marked in the target image by masking, thereby issuing a warning message that there are obstacles in the airport's clear zone, helping airport staff, pilots or air traffic controllers to promptly discover and eliminate potential flight safety hazards, avoid accidents caused by the presence of obstacles, and thus ensure the normal operation of the airport and flight safety.

[0060] Therefore, the airport clear zone obstacle detection method provided in the above embodiment realizes the real-time intelligent detection of clear zone obstacles in an automated manner, without the need for human intervention, and can be detected at any time around the clock. It is particularly suitable for complex airport environments and can better cope with changing conditions such as time, weather, and lighting, and continuously ensure flight safety.

[0061] The airport clear area obstacle inspection method provided in the above embodiment can be applied in the field of low-altitude economy, for example, in low-altitude economic flight activities such as drone flight, aerial logistics distribution, and agricultural spraying. Through accurate obstacle inspection, the method can provide clearer obstacle details for low-altitude economic flight activities, which not only helps to effectively reduce accidents in flight activities, but also optimizes flight paths and provides technical support for the development of low-altitude economy.

[0062] The airport clear zone obstacle inspection method provided in the above embodiment obtains the shielding parameters of the airport surveillance radar; according to the shielding parameters, controls the gimbal to move to the target posture, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; controls the gimbal to stop moving so as to shoot through the shooting device to obtain a target image; performs segmentation processing on the target image through a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; and performs clear zone obstacle inspection on the foreground pixel point set. On the one hand, through the shielding parameters of the airport surveillance radar, the gimbal is reasonably guided to move accurately to the target posture, so that the shooting device carried by the gimbal can take real-time target images that can cover areas that may be blocked by obstacles; on the other hand, through the pre-trained image segmentation model, the foreground pixel point set is accurately segmented from the target image, which can effectively eliminate irrelevant interference factors in the target image, so that only the foreground pixel point set can be focused on obstacle inspection, thereby improving the efficiency and accuracy of obstacle inspection in the clear area. Therefore, the intelligent and automated obstacle inspection in the airport clear area is realized through gimbal control, thereby reducing the cost of obstacle inspection in the airport clear area and improving the accuracy, simplicity and automation of obstacle inspection in the airport clear area.

[0063] See also Figure 4 , Figure 4 It is a schematic block diagram of an airport clear zone obstacle detection device provided in an embodiment of the present application. The airport clear zone obstacle detection device can be configured in a computer device to execute the aforementioned airport clear zone obstacle detection method.

[0064] like Figure 4 As shown, the airport clearance zone obstacle detection device 400 includes: a parameter acquisition module 401, a gimbal control module 402, an image shooting module 403, an image segmentation module 404 and an obstacle detection module 405.

[0065] The parameter acquisition module 401 is used to obtain the shielding parameters of the airport surveillance radar; A gimbal control module 402, used for controlling the gimbal to move to a target posture according to the shielding parameter, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; An image shooting module 403 is used to control the pan / tilt head to stop moving so as to shoot through the shooting device to obtain a target image; An image segmentation module 404 is used to segment the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; The obstacle detection module 405 is used to detect obstacles in the clear area of ​​the foreground pixel set.

[0066] In one embodiment, the shading parameters include a target shading angle.

[0067] In one embodiment, the PTZ control module 402 is specifically used for: The pan / tilt platform is controlled to rotate at a target movement speed until the pitch angle of the pan / tilt platform reaches the target shielding angle.

[0068] In one embodiment, the obstacle detection module 405 is specifically used to: Performing centerline detection processing on the target image to determine the horizontal centerline of the target image; Detecting whether there is a foreground pixel point in the foreground pixel point set whose height exceeds the horizontal center line; In the case where there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal center line, it is determined that there is an obstacle that destroys the airport clear zone; In the case that there is no foreground pixel point in the foreground pixel point set whose height exceeds the horizontal center line, it is determined that there is no obstacle that damages the airport clear zone.

[0069] In one embodiment, the airport clearance zone obstacle detection device 400 further includes an alarm information issuing module, and the alarm information issuing module is used to: Issue a warning message that there are obstacles in the airport clear area.

[0070] In one embodiment, the trained image segmentation model includes a trained YOLOv8Segmentation network.

[0071] In one embodiment, the airport clearance zone obstacle detection device 400 further includes a calibration module, which is used to: The zero-degree position of the pan / tilt is calibrated, and the shooting angle of the shooting device is calibrated.

[0072] Among them, each module in the above-mentioned airport clearance zone obstacle detection device 400 corresponds to each step in the above-mentioned airport clearance zone obstacle detection method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0073] The airport clearance zone obstacle detection device 400 can execute the airport clearance zone obstacle detection method provided in the embodiment of the present application. Therefore, it can achieve the beneficial effects that can be achieved by the airport clearance zone obstacle detection method provided in the embodiment of the present application. Please refer to the previous embodiment for details and will not be repeated here.

[0074] The method and apparatus of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0075] Exemplarily, the above method and apparatus may be implemented in the form of a computer program. The computer program may be implemented in Figure 5 Runs on the computer device shown.

[0076] See also Figure 5 , Figure 5 It is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application.

[0077] See also Figure 5 The computer device includes a processor and a memory connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0078] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0079] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any one of the airport clearance zone obstacle detection methods.

[0080] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0081] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps: Obtain the shielding parameters of the airport surveillance radar; According to the shielding parameter, control the gimbal to move to a target posture, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; Controlling the pan / tilt head to stop moving so as to shoot with the shooting device to obtain a target image; Segmenting the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; The foreground pixel set is checked for obstacles in the clear area.

[0082] In one embodiment, the shading parameters include a target shading angle.

[0083] In one embodiment, when the processor controls the pan / tilt platform to move to a target posture according to the shielding parameter, the processor is used to implement: The pan / tilt platform is controlled to rotate at a target movement speed until the pitch angle of the pan / tilt platform reaches the target shielding angle.

[0084] In one embodiment, when implementing the clear area obstacle screening of the foreground pixel set, the processor is used to implement: Performing centerline detection processing on the target image to determine the horizontal centerline of the target image; Detecting whether there is a foreground pixel point in the foreground pixel point set whose height exceeds the horizontal center line; In the case where there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal center line, it is determined that there is an obstacle that destroys the airport clear zone; In the case that there is no foreground pixel point in the foreground pixel point set whose height exceeds the horizontal center line, it is determined that there is no obstacle that damages the airport clear zone.

[0085] In one embodiment, after implementing the determination that there is an obstacle that damages the airport clearance zone, the processor is further configured to implement: Issue a warning message that there are obstacles in the airport clear area.

[0086] In one embodiment, the trained image segmentation model includes a trained YOLOv8Segmentation network.

[0087] In one embodiment, before controlling the pan / tilt platform to move to a target posture according to the shielding parameter, the processor is further configured to implement: The zero-degree position of the pan / tilt is calibrated, and the shooting angle of the shooting device is calibrated.

[0088] The computer device can execute the airport clearance zone obstacle detection method provided in the embodiment of the present application, and therefore can achieve the beneficial effects that can be achieved by the airport clearance zone obstacle detection method provided in the embodiment of the present application. For details, please refer to the previous embodiments and will not be repeated here.

[0089] An embodiment of the present application also provides a computer-readable storage medium.

[0090] The computer-readable storage medium of the present application stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for checking obstacles in the airport clearance area are implemented.

[0091] The computer-readable storage medium may be an internal storage unit of the airport clearance zone obstacle detection device or computer device described in the above embodiments, such as a hard disk or memory of the airport clearance zone obstacle detection device or computer device. The computer-readable storage medium may also be an external storage device of the airport clearance zone obstacle detection device or computer device, such as a plug-in hard disk, a smart media card (SMC), a secure digital card (SDCard), a flash card, etc., equipped on the airport clearance zone obstacle detection device or computer device.

[0092] Since the computer program stored in the computer-readable storage medium can execute any of the airport clearance zone obstacle detection methods provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the airport clearance zone obstacle detection methods provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0093] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0094] The blockchain referred to in this application is a new application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, etc. Blockchain is essentially a decentralized database, a string of data blocks generated by cryptographic methods. Each data block contains a batch of network transaction information, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, platform product service layer, and application service layer.

[0095] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0096] The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and these modifications or substitutions should be included in the protection scope of the present application.

Claims

1. A method for checking obstacles in an airport clear area, characterized in that: The method comprises: Obtain the shielding parameters of the airport surveillance radar; According to the shielding parameter, control the gimbal to move to a target posture, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; Controlling the pan / tilt head to stop moving so as to shoot with the shooting device to obtain a target image; Segmenting the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; The foreground pixel set is checked for obstacles in the clear area.

2. The method according to claim 1, characterized in that: The shading parameters include a target shading angle.

3. The method according to claim 2, characterized in that The step of controlling the pan / tilt platform to move to a target posture according to the shielding parameter comprises: The pan / tilt platform is controlled to rotate at a target movement speed until the pitch angle of the pan / tilt platform reaches the target shielding angle.

4. The method according to claim 1, characterized in that The checking of obstacles in the clear area of ​​the foreground pixel set includes: Performing centerline detection processing on the target image to determine the horizontal centerline of the target image; Detecting whether there is a foreground pixel point in the foreground pixel point set whose height exceeds the horizontal center line; In the case where there are foreground pixel points in the foreground pixel point set whose height exceeds the horizontal center line, it is determined that there is an obstacle that destroys the airport clear zone; In the case that there is no foreground pixel point in the foreground pixel point set whose height exceeds the horizontal center line, it is determined that there is no obstacle that damages the airport clear zone.

5. The method according to claim 4, characterized in that After determining that there are obstacles that damage the airport clear zone, the method further includes: Issue a warning message that there are obstacles in the airport clear area.

6. The method according to any one of claims 1 to 5, characterized in that: The trained image segmentation model includes a trained YOLOv8 Segmentation network.

7. The method according to any one of claims 1 to 5, characterized in that: Before controlling the pan / tilt platform to move to the target posture according to the shielding parameter, the method further includes: The zero-degree position of the pan / tilt is calibrated, and the shooting angle of the shooting device is calibrated.

8. An obstacle detection device for an airport clear area, characterized in that: The device comprises: A parameter acquisition module is used to obtain the shielding parameters of the airport surveillance radar; A gimbal control module, used for controlling the gimbal to move to a target posture according to the shielding parameter, wherein the setting position of the gimbal is determined based on the setting position of the airport surveillance radar, and the gimbal is used to carry a shooting device; An image shooting module, used for controlling the pan / tilt head to stop moving so as to shoot through the shooting device to obtain a target image; An image segmentation module is used to segment the target image using a pre-trained image segmentation model to obtain a foreground pixel point set of the target image; The obstacle detection module is used to detect obstacles in the clear area of ​​the foreground pixel set.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the airport clear zone obstacle detection method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the airport clearance zone obstacle detection method described in any one of claims 1 to 7 is implemented.

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