Tiny Ship Detection in Remote Sensing Images

Abstract: Due to the difficulties of complicated settings, it is challenging to detect rotating ships in optical remote sensing photographs. Advanced rotational ship detectors currently in use are algorithms which are anchor-based that call for a large number of preconfigured anchors. However, using anchors has these serious issues: Ad hoc heuristics are used to develop anchor characteristics, e.g. size and aspect ratio. Positive samples are only possible for a very tiny percentage of anchors that closely overlap with the bounding boxes of ships.This causes a large disparity in the proportion of positive and negative samples. Poor anchor design will therefore significantly affect the accuracy of detection. This research suggests a unique framework, to solve the aforementioned issues by identifying ships as keypoints in optical remote sensing images. Ship targets in SKNet are modelled using an object's width, height, rotation angle, and centre keypoint. Given this, we develop two distinct modules: orthogonal pooling and softrotate-nonmaximum suppression (NMS). The former seeks to improve the morphological size and centre keypoint predictability, while the latter effectively removes redundant rotated ship detection results.