EST-YOLOv5s: SAR Image Aircraft Target Detection Model Based on Improved YOLOv5s
Min Huang, Weihao Yan, Dai Wen-hui, Jingyang Wang
Abstract
Due to the diversity of aircraft target scale and interference of background strong scattering in synthetic aperture radar (SAR) images, it is a challenge for target detection tasks. In response to these problems, this paper proposes a new SAR image aircraft target detection model named EST-YOLOv5s. The proposed model integrates the Efficient Channel Attention (ECA) mechanism into the C3 module of the backbone network, which enhances the scattering features of aircraft targets and suppresses irrelevant background information without increasing the number of parameters. Secondly, replace the bottleneck module in the last C3 module in the backbone network with the Swin Transformer Block. By using the shifted window partitioning approach to obtain the global perception ability, the problem of missed detection of small objects is improved. Finally, the Task-Specific Context Decoupling (TSCODE) head is used to balance the relationship between classification and regression so that different contextual details can be better utilized. In this paper, the SAR Aircraft Detection Dataset (SADD) is used as the experimental data set to compare with the baseline model YOLOv5s. The experimental outcomes indicate that the recall of the EST-YOLOv5s model reached 94.2%, the precision reached 97.3%, and the mAP@50 reached 97.8%, which were 2.3%, 1.7%, and 1.7% higher than YOLOv5s respectively. Furthermore, our model also meets the real-time requirements in terms of speed and exhibits strong anti-interference ability.