A modified faster R-CNN based on CFAR algorithm for SAR ship detection

Abstract

SAR ship detection is essential to marine monitoring. Recently, with the development of the deep neural network and the spring of the SAR images, SAR ship detection based on deep neural network has been a trend. However, the multi-scale ships in SAR images cause the undesirable differences of features, which decrease the accuracy of ship detection based on deep learning methods. Aiming at this problem, this paper modifies the Faster R-CNN, a state-of-the-art object detection networks, by the traditional constant false alarm rate (CFAR). Taking the objects proposals generated by Faster R-CNN for the guard windows of CFAR algorithm, this method picks up the small-sized targets. By reevaluating the bounding boxes which have relative low classification scores in detection network, this method gain better performance of detection.