Ship detection in large scene SAR images based on target center point regression

Ship target detection in SAR images has important applications in military and civilian fields. However, with the improvement of SAR image imaging capabilities, SAR imaging scenes are getting larger and larger, and there are two difficulties in ship target detection: first, ship targets account for a very small proportion of the entire image, and they are difficult to be separated from the surrounding background; second, the targets of docked ships are usually densely arranged, and it is difficult to distinguish among targets. Currently, commonly used anchor box based detection methods are likely to cause missed detection of ship targets in SAR images of large scenes. In order to solve the above problems, this paper proposes a ship detection method based on the target center point in the large scene SAR images. Based on the fast segmentation of land and sea, the anchor free detector CenterNet is used to locate the center point of the target through key point estimation, and the target boundary is obtained from the center point information regression to achieve target detection, thus effectively avoiding the problem of missing detection based on the anchor frame detection method. Tests based on the public dataset SAR-ship-Dataset show that the method can accurately detect ship targets in SAR images of large scenes, with a detection rate of 92.4%; for densely arranged targets, compared with SSD, YOLO, Fast-RCNN, etc, the method in this paper can also obtain the optimal detection performance.