Robust Real-Time Object Detection Based on Deep Learning for Very High Resolution Remote Sensing Images

Recently, the development of deep learning boosts the object detection for remote sensing images. The existing deep learning methods can be divided into two types. The region-based methods represented by Faster R-CNN have progressive performance in accuracy. However, their computational cost is massive due to the deep Convolutional Neural Network (CNN) backbones, which limits the efficiency. The regression-based methods such as YOLO and Single Shot MultiBox Detector (SSD) are advantageous in speed while the accuracy is not satisfactory. To meet the increasing demand in both speed and accuracy for object detection of remote sensing images, we employ the Reception Field Block Net (RFBNet) detector. It embeds the Receptive Field Block (RFB) module into SSD to obtain better feature representation. The experimental results on NWPU VHR-10 dataset demonstrate that the mAP of RFBNet-512 reaches 91.56%, which outperforms other state-of-the-art networks. Meanwhile, the speed is also competitive.