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

摘要

Deep learning has led to impressive performance on a variety of object detection tasks recently. But it is rarely applied in ship detection of SAR images. The paper aims to introduce the detector based on deep learning into this field. We analyze the advantages of the state-of-the-art Faster R-CNN detector in computer vision and limitations in our specific domain. Given this analysis, we proposed a new dataset and four strategies to improve the standard Faster R-CNN algorithm. The dataset contains ships in various environments, such as image resolution, ship size, sea condition, and sensor type, it can be a benchmark for researchers to evaluate their algorithms. The strategies include feature fusion, transfer learning, hard negative mining, and other implementation details. We conducted some comparison and ablation experiments on our dataset. The result shows that our proposed method obtains better accuracy and less test cost. We believe that SAR ship detection method based on deep learning must be the focus of future research.