TY - GEN
T1 - DeepSign
T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018
AU - Li, Dong
AU - Zhao, Dongbin
AU - Chen, Yaran
AU - Zhang, Qichao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - 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)1.1https://mp.weixin.qq.com/s/IDrTDlJqb2Qx360nhgCXDw (in Chinese, accessed1st January 2018.)
AB - 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)1.1https://mp.weixin.qq.com/s/IDrTDlJqb2Qx360nhgCXDw (in Chinese, accessed1st January 2018.)
UR - http://www.scopus.com/inward/record.url?scp=85053210074&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2018.8489623
DO - 10.1109/IJCNN.2018.8489623
M3 - Conference Proceeding
AN - SCOPUS:85053210074
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 8 July 2018 through 13 July 2018
ER -