TY - GEN
T1 - CNN-based Super-resolution Reconstruction for Traffic Sign Detection
AU - Wang, Fan
AU - Shi, Jianqi
AU - Tang, Xuan
AU - Guo, Jielong
AU - Liang, Peidong
AU - Feng, Yuanzhi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Automatic identification for traffic signs is an important part of intelligent driving and traffic safety. Deep learning has already made a great achievement in traffic sign detection. However, the camera on a car may capture a low resolution and blurry image in certain environments, which make it inaccurate for traffic sign detection. Therefore, we propose a method based on image super-resolution reconstruction for improving the detection rate of traffic signs. Firstly, a low-resolution image is transformed by CNN-based super-resolution network into a high-resolution one. Then, to meet the requirements of on-line processing, we use the generated super-resolution image as input for the detection network with 16 filters in this layer. At last, we separately trained two CNNs for super-resolution reconstruction and traffic sign detection, which reduce the processing time. Experimental results demonstrate that our model can achieve better performance than the existing methods for traffic sign detection.
AB - Automatic identification for traffic signs is an important part of intelligent driving and traffic safety. Deep learning has already made a great achievement in traffic sign detection. However, the camera on a car may capture a low resolution and blurry image in certain environments, which make it inaccurate for traffic sign detection. Therefore, we propose a method based on image super-resolution reconstruction for improving the detection rate of traffic signs. Firstly, a low-resolution image is transformed by CNN-based super-resolution network into a high-resolution one. Then, to meet the requirements of on-line processing, we use the generated super-resolution image as input for the detection network with 16 filters in this layer. At last, we separately trained two CNNs for super-resolution reconstruction and traffic sign detection, which reduce the processing time. Experimental results demonstrate that our model can achieve better performance than the existing methods for traffic sign detection.
KW - convolutional neural networks(CNNs)
KW - super-resolution
KW - traffic sign detection; deep learning
UR - https://www.scopus.com/pages/publications/85080882019
U2 - 10.1109/SSCI44817.2019.9003046
DO - 10.1109/SSCI44817.2019.9003046
M3 - 会议稿件
AN - SCOPUS:85080882019
T3 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
SP - 1208
EP - 1213
BT - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Y2 - 6 December 2019 through 9 December 2019
ER -