Cascade Aggregation Network for Ship Instance Segmentation

Instance segmentation technology has great application in the field of intelligent ships. However, existing methods still have many problems when used directly for ship instance segmentation. For the problems of imprecise bounding box and poor segmentation of detailed contour of ships. We propose a new Cascade Aggregation Network (CAN) for ship instance segmentation. The GIoU loss function is used to optimize the bounding box. And we propose an aggregation segmentation network with multi-scale edge aggregation information can improve mask. Finally, CAN can predict more accurate bounding boxes and generates higher quality masks. Experimental comparison and visual analysis show that our CAN outperforms Cascade Mask R-CNN on the ship instance segmentation dataset.