CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 深度强化学习
DeepSign: Deep Learning based Traffic Sign Recognition
Li, Dong1,2; Zhao, Dongbin1,2; Chen, Yaran1,2; Zhang, Qichao1,2
2018-07
Conference Name2018 IEEE International Joint Conference on Neural Networks
Conference Date8-13 July 2018
Conference PlaceRio de Janeiro, Brazil
Abstract

This paper investigates the traffic sign recognition task with deep learning methods. The proposed algorithm which is called DeepSign includes three modules: a detection module (PosNet) for locating the traffic sign in a static image, a classification module (PatchNet) for classifying the detected image patch, and a temporal filter for correcting the recognition results. The PosNet is a binary object detection convolution neural network which regards all traffic signs as one class and the background as the other class. Different from the traditional works which recognize the traffic sign on the static image, the proposed temporal filter exploits the contextual information to recover the missed detection region and correct the false classification. The experiments validate the effectiveness of the proposed algorithm. It achieved the third place on the traffic sign recognition task in 2017 China intelligent vehicle future challenge (2017 CIVFC).

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23518
Collection复杂系统管理与控制国家重点实验室_深度强化学习
Corresponding AuthorZhao, Dongbin
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Li, Dong,Zhao, Dongbin,Chen, Yaran,et al. DeepSign: Deep Learning based Traffic Sign Recognition[C],2018.
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