摘要
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 detec-tion,but there are still issues with stability,safety and interference from target shadows when directly applied to aerial tar-get 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 oscilla-tion 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 fol-lowing tasks at a level of 0.1 meters.关键词
无人机跟随/目标检测/阴影辨识/中心定位/动态自定位Key words
unmanned aerial vehicle(UAV)following/target detection/shadow recognition/center positioning/dynamic self positioning分类
航空航天
