Vehicle Detection with Partial Anchors in Remote Sensing Images

Fuyan Ma, Bin Sun, Shutao Li, Jun Sun

Abstract: Vehicle detection in remote sensing(RS) images has been an active topic with the development of computer vision in recent years. However, directly applying conventional horizontal anchor-based detection methods in oriented vehicle detection often acquires poor performance. Although rotated anchors have been used to tackle this problem, this design leads to heavy computational cost because of thousands of rotated anchors generated in each level feature map. In this paper, we propose to detect vehicles with partial anchors, which greatly accelerates detection process. The novel Partial Anchors based Detection Network(PADeN) filter out redundant anchors with semantic information. To boost the performance of PADeN, the centerness mask branch is added into the network. The results demonstrate that PADeN significantly outperforms previous approaches in vehicle detection and achieves the mAP of 76.9%.