Unmanned aerial vehicle safety monitoring method and device based on machine vision

Abstract

The invention provides an unmanned aerial vehicle safety monitoring method and device based on machine vision. The method comprises the steps of obtaining three-dimensional point cloud data and image texture information of a target object according to a preset unmanned aerial vehicle; calculating position coordinates of the target object in a three-dimensional space according to the three-dimensional point cloud data, and modeling the surface of the target object to obtain three-dimensional target data; and extracting and identifying abnormal features according to the three-dimensional target data and the image texture information to obtain a safety monitoring result. According to the invention, the unmanned aerial vehicle is introduced to monitor and measure the target object in real time, so that the accuracy and real-time performance of monitoring in a severe environment are ensured; rich three-dimensional space information and two-dimensional image texture information are obtained by using an unmanned aerial vehicle, the three-dimensional space information provides a better basis for accurate extraction of surface defects, corrosion and other features, and the two-dimensional image texture information and the three-dimensional space information are combined to perform comprehensive analysis of multi-dimensional features. And a more reliable basis is provided for accurate identification of the abnormal state.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method and device for unmanned aerial vehicle safety monitoring based on machine vision. Background Art

[0002] Remote objects such as offshore wind turbines are usually located far from the shore, and inspection and measurement of the machine surface are difficult to access. Conventional manual inspection and measurement methods require personnel to board the machine or use large equipment, which is unrealistic for offshore operations. Even if the equipment can be placed on a ship or a floating device, strong winds and waves at sea can cause the ship or floating device to sway, making it impossible for traditional measurement equipment to accurately measure defects on the machine surface.

[0003] In addition, traditional optical, mechanical or electronic sensors may be affected by salt spray corrosion, underwater biological adhesion and other issues, and their reliability and accuracy may be reduced. At the same time, existing visual processing and alarm response have a certain delay and cannot meet the rapid response requirements at critical moments.

[0004] In summary, the existing technology has the problems of low accuracy and insufficient real-time performance. Summary of the invention

[0005] The present invention provides a method and device for unmanned aerial vehicle safety monitoring based on machine vision, which are used to solve the defects of low accuracy and insufficient real-time performance in the prior art, and realize safety monitoring with high accuracy and high real-time performance.

[0006] The present invention provides a method for unmanned aerial vehicle safety monitoring based on machine vision, comprising the following steps: Acquire the three-dimensional point cloud data and image texture information of the target object according to the pre-set UAV; Calculating the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain three-dimensional target data; Abnormal features are extracted and identified based on the three-dimensional target data and the image texture information to obtain a safety monitoring result.

[0007] According to a method for unmanned aerial vehicle safety monitoring based on machine vision provided by the present invention, the position coordinates of the target object in the three-dimensional space are calculated according to the three-dimensional point cloud data and the surface of the target object is modeled to obtain the three-dimensional target data, which specifically includes: Calculate the position coordinates of the target object in the three-dimensional space using the triangulation principle of stereo vision according to the three-dimensional point cloud data; A three-dimensional reconstruction algorithm is used to perform three-dimensional modeling on the surface of the target object according to the position coordinates to obtain three-dimensional target data.

[0008] According to a method for unmanned aerial vehicle safety monitoring based on machine vision provided by the present invention, the position coordinates of the target object in the three-dimensional space are calculated according to the three-dimensional point cloud data and the surface of the target object is modeled to obtain the three-dimensional target data, and then the method further includes: Convert the three-dimensional target data into a preset homogeneous coordinate system to obtain a homogeneous transformation matrix; The target object is tracked in real time according to the secondary transformation matrix.

[0009] According to a method for unmanned aerial vehicle safety monitoring based on machine vision provided by the present invention, abnormal features are extracted and identified according to the three-dimensional target data and the image texture information to obtain a safety monitoring result, which specifically includes: Extracting abnormal features according to the three-dimensional target data and the image texture information; A comprehensive analysis of the target's surface defects, corrosion and cracks is performed based on the abnormal characteristics to obtain safety monitoring results.

[0010] According to a method for unmanned aerial vehicle safety monitoring based on machine vision provided by the present invention, the unmanned aerial vehicle includes a unmanned aerial vehicle equipped with a camera.

[0011] According to a drone safety monitoring method based on machine vision provided by the present invention, three-dimensional point cloud data and image texture information of a target object are obtained according to a preset drone, specifically comprising: A plurality of pre-set drones are used to dynamically or fixedly operate around the target object to obtain the three-dimensional point cloud data and image texture information.

[0012] The present invention also provides a drone safety monitoring device based on machine vision, comprising the following modules: A collection unit, used to obtain three-dimensional point cloud data and image texture information of a target object according to a pre-set UAV; A coordinate unit, used to calculate the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and to model the surface of the target object to obtain three-dimensional target data; The monitoring unit is used to extract and identify abnormal features based on the three-dimensional target data and the image texture information to obtain a safety monitoring result.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a method for unmanned aerial vehicle safety monitoring based on machine vision as described above is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for unmanned aerial vehicle safety monitoring based on machine vision as described in any one of the above is implemented.

[0015] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for unmanned aerial vehicle safety monitoring based on machine vision.

[0016] The drone safety monitoring method and device based on machine vision provided by the present invention obtains the three-dimensional point cloud data and image texture information of the target object according to a pre-set drone; calculates the position coordinates of the target object in three-dimensional space according to the three-dimensional point cloud data and models the surface of the target object to obtain three-dimensional target data; extracts and identifies abnormal features according to the three-dimensional target data and the image texture information to obtain safety monitoring results. The present invention introduces drones to monitor and measure target objects in real time, ensuring the accuracy and real-time nature of monitoring in harsh environments; drones are used to obtain rich three-dimensional spatial information and two-dimensional image texture information, and the three-dimensional spatial information provides a better basis for the accurate extraction of surface defects, corrosion and other features. The two-dimensional image texture information is combined with the three-dimensional spatial information to perform a comprehensive analysis of multi-dimensional features, providing a more reliable basis for the accurate identification of abnormal conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 It is a flow chart of the drone safety monitoring method based on machine vision provided by the present invention.

[0019] Figure 2 It is a structural schematic diagram of the unmanned aerial vehicle safety monitoring device based on machine vision provided by the present invention.

[0020] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Combine the following Figure 1 Describe the drone safety monitoring method based on machine vision of the present invention, Figure 1 FIG. 1 is a flow chart of a method for unmanned aerial vehicle safety monitoring based on machine vision provided by the present invention, such as Figure 1 As shown, the method comprises the following steps: Step 110: Acquire the three-dimensional point cloud data and image texture information of the target object according to the pre-set UAV.

[0023] It should be noted that unmanned aircraft, referred to as "drones", are unmanned aircraft controlled by radio remote control equipment and self-contained program control devices, or are operated completely or intermittently autonomously by on-board computers. Compared with manned aircraft, drones are often more suitable for tasks that are too "dull, dirty or dangerous". Drones can be divided into military and civilian applications according to their application areas. In the military, drones are divided into reconnaissance aircraft and target aircraft. In the civilian field, drones + industry applications are the real rigid demand for drones; their applications in aerial photography, agriculture, plant protection, micro selfies, express delivery, disaster relief, wildlife observation, infectious disease monitoring, surveying and mapping, news reporting, power inspections, disaster relief, film and television shooting, creating romance, etc. have greatly expanded the use of drones themselves, and developed countries are also actively expanding industry applications and developing drone technology.

[0024] The present invention introduces drones into the field of security monitoring, so that it can be further applied in the fields of drone + aerial photography, agriculture, plant protection, micro selfie, express transportation, disaster relief, wildlife observation, infectious disease monitoring, surveying and mapping, news reporting, power inspection, disaster relief, film and television shooting, creating romance, etc.

[0025] The target object refers to the equipment or area to be monitored. In step 110, a drone is used to obtain the three-dimensional point cloud data and image texture information of the target object. The present invention does not limit the specific drone, and the drone can be selected according to the actual situation.

[0026] It can be understood that the three-dimensional point cloud data can be directly obtained by using a laser scanning device, or by using an image acquisition device to obtain image data and using a key point detection algorithm to detect key points in the image, or by using other devices. The present invention is not limited to this.

[0027] Image texture information can be obtained by acquiring image data using an image acquisition device and extracting features using a texture feature extraction algorithm, or it can be obtained using other devices, and the present invention does not limit this.

[0028] The present invention uses a drone to remotely collect data for safety monitoring, avoids the danger of personnel boarding the machine, can work in harsh environments, and provides accurate and reliable measurement results.

[0029] Step 120: Calculate the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and model the surface of the target object to obtain three-dimensional target data.

[0030] In step 120, the position coordinates of the target in the three-dimensional space are calculated based on the collected image or video data, and the target object is modeled to obtain three-dimensional target data.

[0031] It is understandable that the traditional method is mainly based on two-dimensional image analysis, while the present invention uses three-dimensional measurement technology to obtain rich spatial information of the target, thereby providing a better basis for the accurate extraction of surface defects, corrosion and other features. Three-dimensional visual processing methods can better cope with interference factors such as occlusion and shadow in complex scenes, and the robustness and reliability of the overall system have been significantly enhanced.

[0032] Step 130: extract and identify abnormal features based on the three-dimensional target data and the image texture information to obtain a safety monitoring result.

[0033] In step 130, the three-dimensional geometric information in the three-dimensional target data and the two-dimensional image texture features in the image texture information are combined to comprehensively analyze the surface defects, corrosion, cracks, etc. of the target object, extract and identify these abnormal features, and judge whether the state of the target is normal / abnormal.

[0034] The present invention can effectively extract surface defects and corrosion features by analyzing and processing images or videos, and realize comprehensive detection and analysis of target objects. The present invention not only utilizes three-dimensional spatial information, but also organically integrates it with two-dimensional image data. The comprehensive analysis of multi-dimensional features provides a more reliable basis for accurate identification of abnormal conditions.

[0035] Step 120 will be further described below. In some embodiments, calculating the position coordinates of the target object in the three-dimensional space and modeling the surface of the target object according to the three-dimensional point cloud data to obtain the three-dimensional target data specifically includes: Calculate the position coordinates of the target object in the three-dimensional space using the triangulation principle of stereo vision according to the three-dimensional point cloud data; A three-dimensional reconstruction algorithm is used to perform three-dimensional modeling on the surface of the target object according to the position coordinates to obtain three-dimensional target data.

[0036] Specifically, the triangulation principle of stereo vision is used to calculate the position coordinates of the target in three-dimensional space based on the collected image or video data, and advanced three-dimensional reconstruction algorithms are used to achieve accurate three-dimensional modeling of complex target surfaces.

[0037] It should be noted that stereo vision is a technology that uses the principle of triangulation to restore three-dimensional information from two or more images with different perspectives. The following is a specific example of using stereo vision and advanced three-dimensional reconstruction algorithms to accurately model the surface of a target object in three dimensions: According to the three-dimensional point cloud data, matching is performed according to descriptors around the feature points to find corresponding point pairs; Transform the images to the same horizontal line, and calculate the disparity map between the left and right images through a matching algorithm, that is, the horizontal displacement of each pixel; Using the disparity map and the internal and external parameters of the drone, the spatial coordinates of each pixel are calculated through the principle of triangulation; A three-dimensional point cloud is generated according to the calculated spatial coordinates, and point-based reconstruction or surface-based reconstruction is performed according to the three-dimensional point cloud to obtain a three-dimensional model of the target object surface.

[0038] It is understandable that traditional machine vision methods have certain limitations in capturing distant objects, such as insufficient resolution, image noise, etc. The embodiments of the present invention can more accurately capture distant objects through three-dimensional position measurement and transformation of homogeneous coordinate technology, thereby improving the accuracy and reliability of monitoring.

[0039] Furthermore, in some embodiments, the position coordinates of the target object in the three-dimensional space are calculated according to the three-dimensional point cloud data and the surface of the target object is modeled to obtain three-dimensional target data, and then the method further includes: Convert the three-dimensional target data into a preset homogeneous coordinate system to obtain a homogeneous transformation matrix; The target object is tracked in real time according to the secondary transformation matrix.

[0040] Specifically, after obtaining the three-dimensional target data in step 120, the measured three-dimensional target data is converted into a unified homogeneous coordinate system to eliminate the visual distortion caused by the change of target distance and angle, and the homogeneous transformation matrix is ​​used to realize real-time tracking and stable monitoring of the target posture.

[0041] It should be noted that stereoscopic vision is a technology that uses the principle of triangulation to restore three-dimensional information from two or more images with different viewing angles. The following is a specific embodiment of converting the measured three-dimensional target data into a unified homogeneous coordinate system: The rotation and translation of the camera coordinate system relative to the world coordinate system are obtained according to the external parameter matrix of the UAV, and the three-dimensional target data is converted into three-dimensional coordinates of the world coordinate system; Convert the three-dimensional coordinates in the world coordinate system into homogeneous coordinates to obtain a homogeneous transformation matrix; Furthermore, in a specific embodiment, after obtaining the homogeneous coordinate matrix, the specific steps of eliminating the visual distortion caused by the change of target distance and angle include: On the basis of the homogeneous transformation matrix, the perspective transformation matrix is ​​used to correct the perspective distortion caused by the change of target distance and angle.

[0042] Furthermore, in a specific embodiment, using a homogeneous transformation matrix to achieve real-time tracking and stable monitoring of a target posture specifically includes: Get the homogeneous transformation matrix of the time series; The position and posture of the target are analyzed in a unified coordinate system according to the homogeneous transformation matrix at different times for stable monitoring.

[0043] It can be understood that homogeneous coordinate transformation can effectively eliminate the visual distortion of the target caused by changes in distance and angle, and help improve the capture accuracy and stability of long-distance targets.

[0044] Step 130 will be further described below. In some embodiments, extracting and identifying abnormal features based on the three-dimensional target data and the image texture information to obtain a safety monitoring result specifically includes: Extracting abnormal features according to the three-dimensional target data and the image texture information; A comprehensive analysis of the target's surface defects, corrosion and cracks is performed based on the abnormal characteristics to obtain safety monitoring results.

[0045] Specifically, by combining 3D geometric information and 2D image texture features, a comprehensive analysis of the target’s surface defects, corrosion, cracks, etc. is performed, these abnormal features are extracted and identified, and the target’s state is judged to be abnormal.

[0046] The drone is further described below. In some embodiments, the drone includes a drone equipped with a camera. Specifically, the drone equipped with a camera is used as a drone to obtain three-dimensional point cloud data and image texture information. Further, in order to increase the accuracy of monitoring, a drone equipped with a high-resolution camera is selected as the drone.

[0047] Based on the above embodiments, further, in some embodiments, obtaining the three-dimensional point cloud data and image texture information of the target object according to the pre-set drone specifically includes: A plurality of pre-set drones are used to dynamically or fixedly operate around the target object to obtain the three-dimensional point cloud data and image texture information.

[0048] Specifically, a drone equipped with a camera is used as a monitoring station, and multiple monitoring stations are used to dynamically or fixedly operate around the target object, and the collection of three-dimensional point cloud data and image texture information is completed through the collection device of the drone.

[0049] In a specific embodiment, the specific steps of obtaining the three-dimensional point cloud data and image texture information of the target object according to the preset drone include: Register the drone’s acquisition device (laser scanner) and camera to ensure that the laser scanner and camera work in sync so that point cloud data and image texture information can be accurately matched; The drone is made to perform tasks according to the preset flight path to obtain three-dimensional point cloud data and image texture information.

[0050] The embodiment of the present invention utilizes a drone to perform real-time detection and measurement of the machine surface, thereby avoiding the danger of personnel boarding the machine, being able to work in harsh environments, and providing accurate and reliable measurement results.

[0051] The drone safety monitoring method based on machine vision provided by the present invention obtains the three-dimensional point cloud data and image texture information of the target object according to a pre-set drone; calculates the position coordinates of the target object in three-dimensional space according to the three-dimensional point cloud data and models the surface of the target object to obtain three-dimensional target data; extracts and identifies abnormal features according to the three-dimensional target data and the image texture information to obtain safety monitoring results. The present invention introduces drones to monitor and measure target objects in real time, ensuring the accuracy and real-time nature of monitoring in harsh environments; drones are used to obtain rich three-dimensional spatial information and two-dimensional image texture information, and the three-dimensional spatial information provides a better basis for the accurate extraction of surface defects, corrosion and other features. The two-dimensional image texture information is combined with the three-dimensional spatial information to perform a comprehensive analysis of multi-dimensional features, providing a more reliable basis for the accurate identification of abnormal conditions.

[0052] The following is a description of the drone safety monitoring device based on machine vision provided by the present invention. The drone safety monitoring device based on machine vision described below and the drone safety monitoring method based on machine vision described above can be referred to in correspondence with each other. Figure 2 As shown, the device includes the following modules: The acquisition unit 210 is used to acquire the three-dimensional point cloud data and image texture information of the target object according to the preset drone; A coordinate unit 220, used to calculate the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and to model the surface of the target object to obtain three-dimensional target data; The monitoring unit 230 is used to extract and identify abnormal features according to the three-dimensional target data and the image texture information to obtain a safety monitoring result.

[0053] According to a machine vision-based drone safety monitoring device provided by the present invention, the position coordinates of the target object in the three-dimensional space are calculated according to the three-dimensional point cloud data and the surface of the target object is modeled to obtain the three-dimensional target data, which specifically includes: Calculate the position coordinates of the target object in the three-dimensional space using the triangulation principle of stereo vision according to the three-dimensional point cloud data; A three-dimensional reconstruction algorithm is used to perform three-dimensional modeling on the surface of the target object according to the position coordinates to obtain three-dimensional target data.

[0054] According to a machine vision-based unmanned aerial vehicle safety monitoring device provided by the present invention, the position coordinates of the target object in the three-dimensional space are calculated according to the three-dimensional point cloud data and the surface of the target object is modeled to obtain the three-dimensional target data, and then the following is further included: Convert the three-dimensional target data into a preset homogeneous coordinate system to obtain a homogeneous transformation matrix; The target object is tracked in real time according to the secondary transformation matrix.

[0055] According to a machine vision-based drone safety monitoring device provided by the present invention, abnormal features are extracted and identified according to the three-dimensional target data and the image texture information to obtain a safety monitoring result, which specifically includes: Extracting abnormal features according to the three-dimensional target data and the image texture information; A comprehensive analysis of the target's surface defects, corrosion and cracks is performed based on the abnormal characteristics to obtain safety monitoring results.

[0056] According to a machine vision-based unmanned aerial vehicle safety monitoring device provided by the present invention, the unmanned aerial vehicle includes a unmanned aerial vehicle equipped with a camera.

[0057] According to a machine vision-based drone safety monitoring device provided by the present invention, three-dimensional point cloud data and image texture information of a target object are obtained according to a preset drone, specifically including: A plurality of pre-set drones are used to dynamically or fixedly operate around the target object to obtain the three-dimensional point cloud data and image texture information.

[0058] The drone safety monitoring device based on machine vision provided by the present invention obtains the three-dimensional point cloud data and image texture information of the target object according to a pre-set drone; calculates the position coordinates of the target object in three-dimensional space according to the three-dimensional point cloud data and models the surface of the target object to obtain three-dimensional target data; extracts and identifies abnormal features according to the three-dimensional target data and the image texture information to obtain safety monitoring results. The present invention introduces drones to monitor and measure target objects in real time, ensuring the accuracy and real-time nature of monitoring in harsh environments; drones are used to obtain rich three-dimensional spatial information and two-dimensional image texture information, and the three-dimensional spatial information provides a better basis for the accurate extraction of features such as surface defects and corrosion. The two-dimensional image texture information is combined with the three-dimensional spatial information to perform a comprehensive analysis of multi-dimensional features, providing a more reliable basis for the accurate identification of abnormal conditions.

[0059] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 310, a communication interface 320, a memory 330 and a communication bus 340, wherein the processor 310, the communication interface 320 and the memory 330 communicate with each other through the communication bus 340. The processor 310 may call the logic instructions in the memory 330 to execute the drone safety monitoring method based on machine vision, the method comprising: obtaining the three-dimensional point cloud data and image texture information of the target object according to the preset drone; calculating the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain the three-dimensional target data; extracting and identifying abnormal features according to the three-dimensional target data and the image texture information to obtain the safety monitoring result.

[0060] In addition, the logic instructions in the above-mentioned memory 330 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0061] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the machine vision-based drone safety monitoring method provided by the above methods. The method includes: obtaining three-dimensional point cloud data and image texture information of a target object according to a pre-set drone; calculating the position coordinates of the target object in three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain three-dimensional target data; extracting and identifying abnormal features according to the three-dimensional target data and the image texture information to obtain a safety monitoring result.

[0062] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the machine vision-based drone safety monitoring method provided by the above-mentioned methods, the method comprising: acquiring three-dimensional point cloud data and image texture information of a target object according to a pre-set drone; calculating the position coordinates of the target object in three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain three-dimensional target data; extracting and identifying abnormal features according to the three-dimensional target data and the image texture information to obtain a safety monitoring result.

[0063] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0064] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone safety monitoring method based on machine vision, characterized in that: include: Acquire the three-dimensional point cloud data and image texture information of the target object according to the pre-set UAV; Calculating the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain three-dimensional target data; Abnormal features are extracted and identified based on the three-dimensional target data and the image texture information to obtain a safety monitoring result.

2. The method for unmanned aerial vehicle safety monitoring based on machine vision according to claim 1, characterized in that: Calculating the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain three-dimensional target data specifically includes: Calculate the position coordinates of the target object in the three-dimensional space using the triangulation principle of stereo vision according to the three-dimensional point cloud data; A three-dimensional reconstruction algorithm is used to perform three-dimensional modeling on the surface of the target object according to the position coordinates to obtain three-dimensional target data.

3. The method for unmanned aerial vehicle safety monitoring based on machine vision according to claim 1 or 2, characterized in that: The method further comprises: calculating the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and modeling the surface of the target object to obtain three-dimensional target data; and then: Convert the three-dimensional target data into a preset homogeneous coordinate system to obtain a homogeneous transformation matrix; The target object is tracked in real time according to the secondary transformation matrix.

4. The method for unmanned aerial vehicle safety monitoring based on machine vision according to claim 1, characterized in that: Extracting and identifying abnormal features according to the three-dimensional target data and the image texture information to obtain safety monitoring results specifically includes: Extracting abnormal features according to the three-dimensional target data and the image texture information; A comprehensive analysis of the target's surface defects, corrosion and cracks is performed based on the abnormal characteristics to obtain safety monitoring results.

5. The method for unmanned aerial vehicle safety monitoring based on machine vision according to claim 1, characterized in that: The drone includes a drone equipped with a camera.

6. The method for unmanned aerial vehicle safety monitoring based on machine vision according to claim 1 or 5, characterized in that: The 3D point cloud data and image texture information of the target object are obtained according to the pre-set drone, including: A plurality of pre-set drones are used to dynamically or fixedly operate around the target object to obtain the three-dimensional point cloud data and image texture information.

7. A drone safety monitoring device based on machine vision, characterized in that: include: A collection unit, used to obtain three-dimensional point cloud data and image texture information of a target object according to a pre-set UAV; A coordinate unit, used to calculate the position coordinates of the target object in the three-dimensional space according to the three-dimensional point cloud data and to model the surface of the target object to obtain three-dimensional target data; The monitoring unit is used to extract and identify abnormal features based on the three-dimensional target data and the image texture information to obtain a safety monitoring result.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the drone safety monitoring method based on machine vision as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for unmanned aerial vehicle safety monitoring based on machine vision as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for unmanned aerial vehicle safety monitoring based on machine vision as described in any one of claims 1 to 6 is implemented.

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