Ship detection from scratch in Synthetic Aperture Radar (SAR) images

Abstract

ABSTRACT Ship detection in Synthetic Aperture Radar (SAR) images has always been a hot topic for research. The development of Deep Neural Networks (DNNs) has strongly promoted the development of computer vision. DNNs are also increasingly applied to SAR ship detection. However, SAR ship detection still faces the following problems: (i) The network used for detection needs to be pre-trained on ImageNet, but there is a large bias between SAR images and ImageNet, which leads to training bias. (ii) The sizes of ship targets vary greatly, and many DNNs do not perform well on multi-scale and small-size SAR ship detection. Therefore, we have designed a SAR ship detector that does not require pre-training. We use DetNet as the backbone network, adopting stacked convolution instead of down-sampling to solve the problem of small object detection and adopt a feature reuse strategy to improve parameter efficiency. In addition, we introduce several branches in the proposal sub-network to provide multi-scale object detection. In the detection sub-network, we use position-sensitive region of interest pooling to improve the prediction accuracy. Experiments on SAR ship dataset prove that our method performs better than some pre-trained networks for small ship detection and complex background ship detection.