GDB-YOLOv5s: Improved YOLO-based model for ship detection in SAR images

In recent years, deep learning methods were good solutions for object detection in synthetic aperture radar (SAR) images. However, the problems of complex scenarios, large object scale differences and imperfect fine-grained classification in ship detection were intractable. In response, an improved model GDB-YOLOv5s (Improved YOLOv5s model incorporating global attention mechanism (GAM), DCN-v2 and BiFusion) is designed. This model introduces deformable convolution networks (DCN-v2) into the Backbone to enhance the adaptability of the receptive field. It replaces the original Neck's PANet structure with a BiFusion structure to better fuse the extracted multiscale features. Additionally, it integrates GAM into the network to reduce information loss and improve global feature interaction. Experiments were conducted on single-class dataset SSDD and multi-class dataset SRSSD-V1.0. The results show that the GDB-YOLOv5s model improves mean average precision scores (mAP) significantly, outperforming the original YOLOv5s model and other traditional methods. GDB-YOLOv5s also improves Precision-score (P) and Recall-score (R) for fine-grained classification to some extent, thereby reducing false alarms and missed detections. It has been proved that the improved model is relatively effective. Since the general framework YOLO cannot effectively solve the problems of complex scenarios, large object scale differences and imperfect fine-grained classification in the field of ship detection with SAR images, modifications such as introducing deformable convolution DCN-v2, fixing feature extraction network BiFusion and integrating GAM attention mechanism globally, etc. are conducted. A specialized synthetic aperture radar (SAR) image ship detection model GDB-YOLOv5s is obtained. Experiments show that the GDB-YOLOv5s model has good performance in ship detection and fine-grained recognition with SAR images. image