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 integrat...
