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- Title
Target Detection and Real-Time Following for Unmanned Aerial Vehicle.
- Authors
LIU Rongqi; WANG Hongyu; HAN Jiaozhi
- Abstract
With the increasing application of drones in various fields, the demand for drone regulation is also gradually increasing. At the same time, due to the limited computing power and energy of drone platforms, effective detection and following algorithms are particularly important. Currently, deep learning-based methods are very effective for target detection, but there are still issues with stability, safety and interference from target shadows when directly applied to aerial target tracking tasks. To address the problem of shadow interference during target detection, a shadow recognition algorithm based on the HSV color space is proposed, which can segment and identify the shadow areas of the detected object, thus eliminating the interference of shadows on target detection. In order to obtain more accurate 3D position of the target drone, a new positioning algorithm is designed, which combines the center point of the detection box with the relatively fixed center point of the target drone through weighted fusion, reducing the impact of target box size fluctuations on target position estimation. In the obstacle avoidance strategy, drone-related constraints are integrated to avoid excessive oscillation during drone following. Additionally, a dynamic self-localization algorithm is used to detect and correct the control results of the tracked drone in real-time, improving the robustness of the following task. The proposed algorithm is validated through simulation on the Unreal Engine 4 platform and physical experiments, which can keep the accuracy of drone following tasks at a level of 0.1 meters.
- Subjects
DRONE aircraft; COLOR space; TRACKING radar
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 11, p319
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2302-0281