Unmanned aerial vehicle capturing method and device, electronic equipment and storage medium
By collecting the operating sound and images of the drone, combining the Doppler effect and optical flow algorithm for analysis, the problem of inaccurate identification of drones in the existing technology is solved, and efficient drone capture is achieved.
CN119989257APending Publication Date: 2025-05-13XINCHUANG GREAT WALL (BEIJING) TECH CO LTD
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Patent Information
- Application Number
- CN202411970531.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-05-13
AI Technical Summary
Technical Problem
Existing drone capture methods are difficult to ensure the accuracy of drone identification, resulting in a low capture success rate.
Method used
By collecting the operating sound and images of the drone, the motion state analysis and identification process is performed using the Doppler effect algorithm and optical flow algorithm to determine the position of the drone and capture it.
Benefits of technology
It significantly improves the accuracy of drone identification and the success rate of capture, and ensures the precise determination of drone location through multi-dimensional monitoring.
✦ Generated by Eureka AI based on patent content.
Abstract
According to the unmanned aerial vehicle capturing method and device, the electronic equipment and the storage medium, the operation sound and image of the unmanned aerial vehicle are collected, the recognition reliability is improved by using double information sources, and whether the target object is the unmanned aerial vehicle or not can be more accurately judged in combination with the sound features and the image features, so that the recognition accuracy is remarkably improved. In addition, the motion state of the unmanned aerial vehicle is analyzed by using a Doppler effect algorithm, and the operation speed of the unmanned aerial vehicle is identified in combination with an optical flow algorithm, thereby realizing comprehensive monitoring of the motion state of the unmanned aerial vehicle. The multi-dimensional monitoring mode not only improves the accuracy of unmanned aerial vehicle position determination, but also provides powerful data support for subsequent unmanned aerial vehicle capture, so that the accuracy of unmanned aerial vehicle identification and the success rate of capture can be significantly improved.
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