Ship detection based on improved faster R-CNN in SAR images

Ship detection in synthetic aperture radar (SAR) images is receiving more and more attention. At the same time, the need for high-precision and intelligent ship detection is becoming more and more urgent. To further improve the detection performance in SAR images, this paper proposes an improved Faster R-CNN based on feature pyramid network (FPN) and cascade network for SAR ship detection. First, Faster R-CNN is used as a baseline network to realize ship detection. Then, FPN was used to deal with the multi-scale problem of ships. Finally, the cascade network is used to further improve the ship detection performance. Experimental results on the public SAR ship detection dataset (SSDD) show that this method has better detection performance than YOLOv2, Faster R-CNN, Cascade R-CNN and RetinaNet.