Deconv R-CNN for Small Object Detection on Remote Sensing Images

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.