  Object detection and instance segmentation in remote sensing images is a
fundamental and challenging task, due to the complexity of scenes and targets.
The latest methods tried to take into account both the efficiency and the
accuracy of instance segmentation. In order to improve both of them, in this
paper, we propose a single-shot convolutional neural network structure, which
is conceptually simple and straightforward, and meanwhile makes up for the
problem of low accuracy of single-shot networks. Our method, termed with
SSS-Net, detects targets based on the location of the object's center and the
distances between the center and the points on the silhouette sampling with
non-uniform angle intervals, thereby achieving abalanced sampling of lines in
mask generation. In addition, we propose a non-uniform polar template IoU based
on the contour template in polar coordinates. Experiments on both the Airbus
Ship Detection Challenge dataset and the ISAIDships dataset show that SSS-Net
has strong competitiveness in precision and speed for ship instance
segmentation.
