@inproceedings{224443c70f9a45c5a102442b4eb2b5d4,
title = "Faster R-CNN for small traffic sign detection",
abstract = "Traffic sign detection is essential in autonomous driving. It is challenging especially when large proportion of instance to be detected are in small size. Directly applying state-of-the-art object detection algorithm Faster R-CNN for small traffic sign detection renders unsatisfactory detection rate, while a higher accuracy will be performed if the input images are upsampled. In this paper, we first investigate Faster R-CNN{\textquoteright}s network architecture, and regard its weak performance on small instances as improper receptive field. Then we augment its architecture according to receptive field with a higher accuracy achieved and no obvious incremental computational cost. Experiments are conducted to validate the effectiveness of proposed method and give an comparison to the state-of-the-art detection algorithms on both accuracy and computational cost. The experimental results demonstrate an improved detection accuracy and an competitive computing speed of the proposed method.",
author = "Zhuo Zhang and Xiaolong Zhou and Sixian Chan and Shengyong Chen and Honghai Liu",
year = "2017",
month = dec,
doi = "10.1007/978-981-10-7305-2_14",
language = "English",
isbn = "978-981-10-7304-5",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "155--165",
editor = "Jinfeng Yang and Qingshan Liu and Liang Wang and Xiang Bai and Qinghua Hu and Ming-Ming Cheng and Deyu Meng",
booktitle = "Computer Vision - 2nd CCF Chinese Conference, CCCV 2017, Proceedings",
address = "Germany",
note = "CCF Chinese Conference on Computer Vision, CCCV 2017 ; Conference date: 11-10-2017 Through 14-10-2017",
}