Knowledge Commons of Institute of Automation,CAS
DeepSign: Deep Learning based Traffic Sign Recognition | |
Li, Dong1,2; Zhao, Dongbin1,2; Chen, Yaran1,2; Zhang, Qichao1,2 | |
2018-07 | |
会议名称 | 2018 IEEE International Joint Conference on Neural Networks |
会议日期 | 8-13 July 2018 |
会议地点 | Rio de Janeiro, Brazil |
摘要 | 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). |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23518 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
通讯作者 | Zhao, Dongbin |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Dong,Zhao, Dongbin,Chen, Yaran,et al. DeepSign: Deep Learning based Traffic Sign Recognition[C],2018. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2018.IJCNN_LiZhaoChe(6951KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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