Contour deformation network for instance segmentation

• GCN-based contour deformation network is proposed. • The refined contour of an object mask is achieved for instance segmentation . • To deal with various sizes of objects in scenes, adaptive deformation-scale selection strategy presented. • Automatically constructs the local neighborhood graph and selects multiscale features. • Extensive experimental results provided to demonstrate the performance of the proposed network. To improve the precision of the contour in instance segmentation, this study proposes an iterative contour deformation network (CD-Net) based on a graph convolutional network (GCN). The proposed method treats the segmentation results of the Mask R-CNN model as the initial contours and refines the instances contour iteratively. Specifically, a contour point set is first sampled from the initial contour. Considering the various sizes of the instances, and according to the size of corresponding bounding boxes determined by the Mask R-CNN, a local neighborhood graph is constructed for each selected contour point. Subsequently, multi-scales features are automatically selected and combined with features learned in Mask R-CNN for each point in the local neighborhood graph. The local neighborhood graphs with features are then fed into the GCN to learn the deformation vectors, and the instance contours are refined accordingly. Finally, the refined contour is treated as the initial contour, and the above process is repeated to obtain the final instance contours. The experimental results on the COCO and Cityscapes datasets demonstrate that the proposed method achieves state-of-the-art performance.