Transformer Enhanced YOLO Network for Ship Detection in Optical Remote Sensing Images

Ship detection in optical remote sensing images poses challenges such as complex scenes, large target scale differences, and imperfect fine-grained classification. Deep learning methods like YOLO have provided effective solutions. With emerging detection approaches like Transformers achieving new breakthroughs in accuracy, this study proposes an enhanced detection model by incorporating Transformer into YOLO network. The model replaces the Backbone of YOLOv5s with SwinTransformer-Tiny and refines the Loss function to Wise-IoU, tailored for remote sensing images and ship targets. Experimental verification on self-curated single-category dataset LEVIR-ship and multi-category dataset HRSC-4 shows that the comprehensive metric mAP50 values have increased from 93.8% and 96.2% to 95.2% and 96.5%, respectively. Both precision (P) and recall (R) values have significantly improved. There is also some enhancement in fine-grained classification metrics on the HRSC-4 dataset. The newly improved model demonstrates good performance.