A Scalable Network for Tiny Object Detection Based on Faster RCNN

Abstract

In this paper, we propose a scalable network for tiny object detection based on Faster RCNN. Compared with the previous feature extraction network, our network can be better applied to tiny objects. In the process of feature extraction, the feature representation of large object will be strengthened, and the important tiny object information is ignored. By merging the feature maps output from different filters on the same layer, different sizes of targets will be captured. Then, not only considers the width of the network, but also realizes the deep integration of the network, which can avoid that the network is too deep to filter out tiny target information. Finally, by optimizing the algorithm for tiny objects based on deep learning, we achieved the best results with the accuracy rate of 34.1% on the Tsinghua-Tencent 100K.