基于YOLOX-drone的反无人机系统抗遮挡目标检测算法

Anti-occlusion target detection algorithm for anti-UAV system based on YOLOX-drone

为解决现实场景下无人机目标被部分遮挡,导致不易检测问题,本文提出了基于YOLOX-S改进的反无人机系统目标检测算法YOLOX-drone.首先,建立无人机图像数据集;其次,搭建YOLOX-S目标检测网络,在此基础上引入坐标注意力机制,来增强无人机的目标图像显著度,突出有用特征抑制无用特征;然后,再去除特征融合层中自下而上的路径增强结构,减少网络复杂度,并设计了自适应特征融合网络结构,增强有用特征的表达能力,抑制干扰,提升检测精度.在DUT-AntiUAV数据集上的测试结果表明:YOLOX-drone与YOLOX-S、YOLOv5-S和YOLOX-tiny相比,平均准确率(IoU=0.5)提升了3.2%、4.7%和10.1%;在自建的无人机图像数据集上的测试结果表明:YOLOX-drone与原YOLOX-S目标检测模型相比,在无遮挡、一般遮挡、严重遮挡情况下,平均准确率(IoU=0.5)分别提高了2.4%、2.1%和6.4%,验证了改进的算法具有良好的抗遮挡检测能力.

With the development and advancement of science and technology,the development and innovation of unmanned aerial vehicle(UAV)technology and products have brought great convenience to people in the fields of aerial photography,plant protection,electric cruise,and so on,but the development of UAVs also brings a series of management problems.Therefore,as a key part of the anti-UAV system,research into effective UAV detection is a pressing issue that must be addressed.In public environments such as parks,stadiums,and schools,the detection and tracking of UAV targets become more difficult due to their inherent characteristics and environmental factors.For example,under the occlusion of background interferences such as trees,buildings,and light,the target detection algorithm is unable to extract the effective features of the UAV target,resulting in target detection failure.It is of great significance to study the anti-occlusion target detection and tracking algorithm of anti-UAV systems for situations where UAVs cannot be successfully detected due to occlusion.This study proposes an improved anti-UAV system target detection algorithm YOLOX-drone based on YOLOX-S to solve the problem of the UAV being deformed and partially occluded in complex scenes,which makes it difficult to identify.First,in this study,numerous occluded drone images are collected in complex scenes,and the drone pictures are downloaded online for occlusion processing.The drone images were labeled to establish a UAV image dataset.Second,the YOLOX-S target detection network was constructed.On this premise,the coordinate attention mechanism is introduced to improve the saliency of the target image when the drone is obscured by highlighting useful features and suppressing useless ones.Then,the bottom-up path enhancement structure in the feature fusion layer is removed to reduce the network complexity,and an adaptive feature fusion network structure is designed to improve the expression ability of useful features,suppress interference,and improve detection accuracy.First,experiments were conducted on the Dalian University of Technology Anti-UAV dataset,and the experimental results show that YOLOX-drone improved average accuracy(IOU=0.5)by 3.2%,4.7%,and 10.1%compared to YOLOX-S,YOLOv5-S,and YOLOX-tiny,respectively.Then,experiments were conducted on the self-built UAV image dataset,and YOLOX-drone improved the average accuracy(IOU=0.5)by 2.4%,2.1%,and 6.4%in the cases of no occlusion,general occlusion,and severe occlusion,respectively,when compared with the original YOLOX-S target detection model.This demonstrates that the improved algorithm has good anti-occlusion detection ability.