Ensemble R-FCN for object detection

Abstract

This paper presents an Ensemble R-FCN framework for object detection. Specifically, we mainly make three contributions to our detection framework: (1) we augment the training images for R-FCN when facing the limited training samples and small object. (2) We further introduce several enhancement schemes to improve the performance of the single R-FCN. (3) An ensemble R-FCN is proposed to make our detection system more robust by combining different feature extractors and multi-scale inference. Experimental results demonstrate the advantages of the proposed method. Especially, our method achieved the performance of AP score 0.829 which ranked No. 1 among over 360 teams in Ucar Self-driving deep learning Competition.