Small Object Detection in Optical Remote Sensing Video with Motion Guided R-CNN
Jie Feng, Yuping Liang, Zhanwei Ye, Xiande Wu, Dening Zeng, Xiangrong Zhang, Xu Tang · 2020
Deep learning (DL) based object detection methods have been making great achievements for natural images, which guides the vehicle detection of optical remote sensing videos (ORSV). Compared with natural images, objects in ORSV are smaller and blurrier, and most of vehicles are crowded. Thus, it is difficult for DL to detect these small objects only using the single-frame image. To address this problem, a motion guided R-CNN (MG-RCNN) is proposed. In MG-RCNN, motion information from consecutive frames is extracted by the mean differencing method and merged into apparent information to obtain motion-related discriminative features. Then, high-quality proposals are generated on the feature maps by mini-region proposal network (MRPN). For small targets, an improved loss function is defined by incorporating smooth factor, which makes the regression of shapes more stable. Experiments on ORSV demonstrate the proposed method shows superior detection performance over state-of-the-art deep learning methods.