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Global Mask R-CNN for marine ship instance segmentation by Yuxin Sun; Li Su; Yongkang Luo; Hao Meng; Wanyi Li; Zhi Zhang; Peng Wang; Wen Zhang is a Computer Science article available to read on EtoBox.

What is Global Mask R-CNN for marine ship instance segmentation about?

Instance segmentation technology can provide accurate and efficient segmentation methods for visual perception of marine scenes, especially in the development of unmanned ships. However, the community lacks suitable open-source datasets. In order to address the problem of insufficient datasets, an instance segmentation dataset for the marine ship was collected and labeled. Our dataset, named MariShipInsSeg, consists of 4k high-quality visible light marine ship images with 8,413 instances. Due to marine ships being photographed far away, which causes ship objects with less detail information. Therefore, a global method is adopted to make full use of global location and semantic information, which is helpful for ship instance segmentation. We proposed a new method called Global Mask R-CNN (GM R-CNN), which utilized Precise RoI Pooling and Global Mask Head aiming to preserve global information of instances for improving the performance of ship instance segmentation. Experiments on the challenging MS COCO dataset and MariShipInsSeg dataset show that Global Mask R-CNN achieves state-of-the-art performance. Without any bells and whistles, the proposed GM R-CNN achieves 38.7% mask AP on M

Who reads Global Mask R-CNN for marine ship instance segmentation?

Author

Yuxin Sun; Li Su; Yongkang Luo; Hao Meng; Wanyi Li; Zhi Zhang; Peng Wang; Wen Zhang

Publisher

Elsevier BV

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)