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

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.

1. Introduction

The detection of geospatial objects is a fundamental and challenging problem for analyzing and understanding remote sensing images. Recently, deep learning has improved the performance significantly in various image processing techniques [1–6], and object detectors with convolutional neural network (CNN) have achieved the state-of-the-art accuracy. However, these successful detecting methods are difficult to exhibit their excellent performance on remote sensing images, since there are obvious differences between remote sensing images and natural scenes.