Deconv R-CNN for Small Object Detection on Remote Sensing Images
Wei Zhang, Shihao Wang, Sophanyouly Thachan, Jingzhou Chen, Yuntao Qian · 2018
Small object detection has drawn increasing interest in computer vision and remote sensing image processing. The Region Proposal Network (RPN) methods (e.g., Faster R-CNN) have obtained promising detection accuracy with several hundred proposals. However, due to the pooling layers in the network structure of the deep model, precise localization of small-size object is still a hard problem. In this paper, we design a network with a deconvolution layer after the last convolution layer of base network for small target detection. We call our model Deconv R-CNN. In the experiment on a remote sensing image dataset, Deconv R-CNN reaches a much higher mean average precision (mAP) than Faster R-CNN.