LA-YOLO: an effective detection model for multi-UAV under low altitude background
Jun Ma, ShangCheng Huang, Dongyang Jin, Xuzhe Wang, Longchao Li, Yan Guo
Abstract
Abstract Detecting unmanned aerial vehicles (UAVs) in various environments and conditions is highly demanded in applications, and for solving the problem of detecting UAVs under low altitude background, we propose a high performance and effective LA-YOLO network by integrating the SimAM attention mechanism and introducing a fusion block with the normalized Wasserstein distance. By recording images of multi-UAV under low altitude background and annotating them, we construct a dataset called GUET-UAV-LA to evaluate the performance of the proposed network. Using the GUET-UAV-LA dataset and public datasets, the experiments validate the effectiveness of the proposed network and show that LA-YOLO can improve mAP by up to 5.9% compared to the existing networks.