关键词: 低慢小目标, 反无人机系统, 深度学习, 小目标检测, 旋翼无人机检测, 数据增强, 特征融合, YOLOv5

Abstract: The detection of low-altitude slow-speed small targets has always been the focus and difficulty in the field of early warning detection.At present,the mainstream target detection algorithms based on neural networks are mainly designed to be applied to VOC dataset or COCO dataset,and the detection accuracy is not ideal in specific scenarios.Aiming at the specific detection scene of small drones target detection in complex background,a small drones target detection algorithm based on improved YOLOv5 is proposed.First,a small target detection layer is added to obtain a large-sized shallow feature map,thereby improving the detection ability of the algorithm for small targets.Secondly,for the problem of different sizes of small drones,K-means+＋clustering algorithm is used to detect the prior frame.The size of the inspection frame is optimized and matched with each feature layer.Finally,the Mosaic-SOD methods of data augmentation and improved loss function are used to enhance the algorithm’s ability to perceive small targets and improve the efficiency of network training.The improved algorithm is applied to the target detection of small drones in complex background.Experimental results show that compared with the original YOLOv5 algorithm,the proposed algorithm has higher detection accuracy and characteristics in target detection of small rotor UAV.The extraction capability,although the detection speed has decreased to a certain extent,can still meet the real-time requirements by detecting the visible light video stream.

Key words: Low-Slow-Small target, Anti-UAV system, Deep learning, Small object detection, Small drones detection, Data augmentation, Feature fusion, YOLOv5(You Only Look Only version 5)