ROS-Det: Arbitrary-Oriented Ship Detection in High Resolution Optical Remote Sensing Images via Rotated One-Stage Detector

Abstract

To address these problems, namely, arbitrary orientations, various sizes, and dense distributions of ship detection, we propose an arbitrary-oriented ship detection method via rotated single-stage detector (ROS-Det), which integrates a feature pyramid network (FPN) based on an improved ResNet50, rotated anchors, a classification network, and a regression network together. Firstly, to improve robustness against various sizes of ships, the FPN is used to fusion multiscale convolutional feature maps. Through several tweaks, the improved ResNet50 can receive more information and reduce the computational cost. Secondly, for the purpose of arbitrary-oriented ship detection, rotated anchors, skew intersection over union (IoU), and skew non-maximum suppression (NMS) are introduced to RetinaNet. Then, on account of the disadvantages that the arbitrary-oriented object detection methods usually cause loss discontinuity problem, we improve the traditional smooth L1 loss function by introducing an IoU constant factor. Finally, based on several techniques such as data augmentation and transfer learning, we achieve ship detection on a public ship dataset HRSC2016. Through comparison experiments, we have analyzed and discussed the validity of our proposed ROS-Det, which achieves the state-of-the-art performance.