Ship Instance Segmentation in Foggy Scene
Yuxin Sun, Li Ping Su, Haohao Cui, Yu‐Sheng Chen, Shouzheng Yuan · 2021
Fog is a common weather phenomenon at sea. In foggy conditions, image quality collected by imaging equipment is relatively poor, and ship targets are blurred. This interference seriously affects the accuracy of ship instance segmentation. To this end, we propose a new instance segmentation with channel attention module, called CondInstAtt for ship segmentation in foggy scene. Meanwhile, to solve data scarcity in foggy scene, a foggy image simulation method based on an atmospheric scattering model for the marine scene is used. We also label the instance segmentation dataset with 2929 ship images in fog. Finally, the numerical and visual results clearly show that our method outperforms a few recent methods including Mask RCNN and CondInst.