Rotated Ship Detection Based On Dense Points in High Resolution Remote Sensing Images

The rapid development of remote sensing technology has provided convenient conditions for obtaining abundant research data. The use of visible light remote sensing images for ship detection has profound significance in the fields of port management, maritime rescue, and military investigation. Our paper focuses on the shortcomings of current way of ship positioning expression and uses deep learning to study the rotated ship detection task. A kind of ship detection algorithm based on dense points related to the position of ships is proposed to solve the problem of angle boundary and vertex sorting ambiguity in current rotated object detection methods. Our method achieves 92.4 mAP on the HRSC2016 dataset, ranking among the top in similar studies.