Multi-level Feature Selection for Oriented Object Detection

Horizontal object detection has made significant progress, but the representation of horizontal bounding box still has application limitations for oriented objects. In this paper, we propose an end-to-end rotation detector to localize and classify oriented targets precisely. Firstly, we introduce the path aggregation module, to shorten the path of feature propagation. To distribute region proposals to the most suitable feature map, we propose the feature selection module instead of using selection mechanism based on the size of region proposals. What's more, for rotation detection, we adopt eight-parameter representation method to parametrize the oriented bounding box and we add a novel loss to handle the boundary problems resulting from the representation way. Our experiments are evaluated on DOTA and HRSC2016 datasets.