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With the development of deep convolutional neural network, the performance of instance segmentation algorithms has been significantly improved. However, it is difficult to implement quick and accurate near-shore ship segmentation due to the complex background and arbitrary ship orientation, which makes segmentation challenging. To improve the segmentation efficiency of near-shore ships, this paper proposes a ship segmentation network based on Polar Mask, named I-Polar Mask. Specifically, we construct a Specific Polar Template (SPT) and Oriented Polar IoU (Intersection over Union) to better match the ship contour. Furthermore, oriented polar centre-ness was designed to reduce the weight of low-quality masks more effectively. In addition, a Context Information Extraction Module (CIEM) is built to reduce the influence of complex backgrounds and make the segmentation more accurate. To verify the effectiveness of the proposed algorithm, we collected 1015 images of near-shore areas from Google Maps and labelled them with Labelme to construct a ship instance segmentation dataset, called I-Ship. Extensive experiments on I-Ship show that the AP value of I-Polar Mask is improved by 5.9% compared with Polar Mask, which is a significant improvement. Compared with advanced methods, I-Polar Mask outperforms both quantitative and qualitative aspects.

Near-Shore ship segmentation based on I-Polar Mask in remote sensing