An Improved Lightweight YOLOv5 for Remote Sensing Images

Achieving real-time accurate detection in remote sensing images, which exhibit features such as high resolution, small targets, and complex backgrounds, remains challenging due to the substantial computational demands of existing object detection models. In this paper, we propose an improved remote sensing image small object detection method based on YOLOv5. In order to preserve high-resolution features, we remove the Focus module from the YOLOv5 network structure and introduce RepGhostNet as a feature extraction network to enhance both accuracy and speed. We adopt the BiFormer prediction head for more flexible computational allocation and content perception, and employ the Normalized Wasserstein Distance (NWD) metric to alleviate IoU's sensitivity to small objects. Experimental results show that our proposed method achieves mAP scores of 75.54% and 75.65% on the publicly available VEDAI and DIOR remote sensing image datasets, respectively, with significantly fewer parameters and FLOPs. Our approach effectively balances accuracy and speed compared to other models.