Cropping Region Proposal Network Based Framework for Efficient Object Detection on Large Scale Remote Sensing Images

Abstract

It is very difficult to directly detect objects on the entire large scale remote sensing image, due to the limited GPU memory. Moreover, there are no objects of interest in most areas of such a huge image, thus a lot of computational costs is wasted in dealing with these vain areas. Therefore, this paper proposes a Cropping Region Proposal Network (CRPN), which includes a weak semantic RPN for quickly locating interesting regions, and a dual-scale strategy for generating effective cropping regions. Cropping regions consist of small and large cropping scales for detecting various-scale objects including very small and very large objects, which is hard for existing methods. CRPN helps to detect effective regions of remote sensing image. Meanwhile, it is also modularized and can be easily connected with mainstream detectors to form an end-to-end detecting framework. Experiments on public DOTA dataset show that our CRPN is effective for filtering invalid regions to greatly reduce the computation burden, and helps to achieve more accurate object detection on large scale remote sensing images.