PEGNet: An Enhanced Ship Detection Model for Dense Scenes and Multiscale Targets

Abstract

In recent years, synthetic aperture radar (SAR) ship detection has seen significant improvements due to the rapid development of deep learning. However, when ship targets are densely arranged or exhibit multiscale variations, there are still issues such as significant differences in aspect ratios, resulting in false alarms, missed detections, and low detection accuracy. To overcome these challenges, this letter introduces a novel detection model, PEGNet, based on Faster R-CNN. First, to identify ship targets at different scales, the path aggregation feature pyramid network (PAFPN) was integrated into the feature fusion structure, which enhances the network’s feature representation and robustness. Second, efficient multiscale attention (EMA) was employed to strengthen detection accuracy by reducing noise interference and enhancing feature stability. Third, the guided anchoring region proposal network (GA-RPN) was introduced to produce anchors that more accurately reflect the actual positions and scales of targets, which improves localization precision and lowers the missed detection rate. The performance of PEGNet was tested on the SSDD and high-resolution SAR images dataset (HRSID) datasets, achieving mAP scores of 71.1% and 67.9%, respectively. Compared to the baseline network, this represents improvements of 2.5% and 7.6%. This result highlights the method’s superior performance compared to other approaches.