Inshore ship detection based on improved Faster R-CNN

Ship object detection has a wide range of applications in both military and civilian. In the military, as an important and classical object in the field of military, the quick and accurate detection of ship is closely related to the success or failure of the battle. Similarly, as survival tools in civilian use, whether the number and location of ships can be quickly and accurately identified has a strong impact on marine rescue. In recent years, various object detection methods have been applied to ship detection, which include traditional methods and methods based on deep learning. However, due to the complicated background of inshore ships, the detection process is easily interfered by the buildings on the shore. As a result, the effects of ship detection are not yet satisfactory and researchers are making efforts to further improve these methods. In this paper, we propose an inshore ship detection method based on improved Faster R-CNN. As a typical and benchmark framework in the detection field, Faster R-CNN is chosen as the base pipeline. On the basis of this method, Soft NMS and Focal Loss are added with consideration of the characteristics that sizes of ships and distances between them are all different. Experiments on various data sets validate the effectiveness of our improvement. The mean Average Precision on our own 200 ship dataset has increased by 6% to 80.4%. The mean Average Precision on the public HRSC2016 ship dataset has increased by 1.1% to 84.5%.