Ship Detection Using Transfer Learned Single Shot Multi Box Detector

¹ National Lab for Parallel and Distributed Processing, National University of Defense Technology, Changsha, China
² Institute of Electronic Information Warfare, Naval University of Engineering, Wuhan, China

^* nieguhong15@nudt.edu.cn
^** zhangpeng14f@nudt.edu.cn
^*** niuxin@nudt.edu.cn
^**** yongdou@nudt.edu.cn
^***** xcyphoenix@nudt.edu.cn

Abstract

Ship detection in satellite images is a challenging task. In this paper, we introduce a transfer learned Single Shot MultiBox Detector (SSD) for ship detection. To this end, a state-of-the-art object detection model pre-trained from a large number of natural images was transfer learned for ship detection with limited labeled satellite images. To the best of our knowledge, this could be one of the first studies which introduce SSD into ship detection on satellite images. Experiments demonstrated that our method could achieve 87.9% AP at 47 FPS using NVIDIA TITAN X. In comparison with Faster R-CNN, 6.7% AP improvement could be achieved. Effects of the observation resolution has also been studied with the changing input sizes among 300 × 300, 600 × 600 and 900 × 900. It has been noted that the detection accuracy declined sharply with the decreasing resolution that is mainly caused by the missing small ships.