Inshore Ship Detection Based on Mask R-CNN
Shanlan Nie, Zhiguo Jiang, Haopeng Zhang, Bowen Cai, Yuan Yao · 2018
Inshore ship detection is a popular research domain for optical remote sensing image understanding with many applications in harbor management. However, recent approaches on inshore ship detection depend heavily on hand-crafted features, which need a complicated procedure. In this paper, we propose a new method to achieve inshore ship detection based on Mask R-CNN. We introduce Soft-Non-Maximum Suppression (Soft-NMS) into our framework to improve the robustness to nearby inshore ships. Both battleships and merchantships can be detected in our framework. Furthermore, our framework can also obtain the binary masks of inshore ships. Experimental results on a dataset collected from Google Earth have quantitatively and qualitatively demonstrated the effectiveness of our approach.