An Anchor-free Object Detection Network for Arbitrarily-orientated Ships in Large-scale Remote Sensing Images

Abstract

ABSTRACT Reliable and efficient ship detection is still a challenging task for large-scale remote sensing images with complex background. Since ships in remote sensing images are usually densely distributed with different sizes and uncertain directions, adopting the common horizontal bounding box to detect the inclined ships with large aspect ratio will lead to inaccurate detection. In this letter, a multitask learning object detection framework called MSRDNet is proposed to detect ships with arbitrarily orientations. Convolutional features are extracted by the backbone with parallel attention residual block (PARB), which combines spatial and channel attention module. Then, a lightweight classifier is designed as a subtask to filter out a large number of background patches. Moreover, the anchor-free detector with orientation regression is designed as the other subtask for the inclined and densely distributed ships. The proposed algorithm and comparative methods are tested on two public remote sensing image data sets DOTA and HRSC2016. The experimental results demonstrate that the proposed algorithm outperforms all the compared methods. The code is available from https://github.com/Sdy344/paper_code .