|Table of Contents|

Traffic sign recognition method based on graphical model and convolutional neural network(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2016年05期
Page:
122-131
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Traffic sign recognition method based on graphical model and convolutional neural network
Author(s):
LIU Zhan-wen1;  ZHAO Xiang-mo1;  LI Qiang1;  SHEN Chao1;  WANG Jiao-jiao2
1. School of Information Engineering, Chang’an University, Xi’an 710064, Shaanxi, China; 2. School of Electronic and Control Engineering, Chang’an University, Xi’an 710064, Shaanxi, China
Keywords:
traffic control;  traffic sign;  saliency detection;  convolutional neural network;  pre-training strategy
PACS:
U491.52
DOI:
-
Abstract:
In order to improve the recognition robustness of traffic sign, a recognition method of traffic sign based on graphical model and convolutional neural network(CNN)was proposed, and a application-oriented recognition system of traffic sign based on the regions with convolutional neural network(R-CNN)was set up. A graphical model based on UCM superpixel region was constructed, the multi-scales information from bottom to up was used efficiently, and a hierarchical saliency detection method based on graphical model was proposed to extract the interest regions of traffic sign. The candidate interest regions were processed for the feature extraction and classification of traffic sign with CNN. Detecting result indicates that aiming at the speed-limited signs, the graphical model based on UCM superpixel can get more larger-scale construction information of upper saliency map contrasting to the graphical model based on simple linear iterative cluster(SLIC)superpixel. Because the hierarchical saliency detection model based on the prior location restriction and the local properties combines the detail information of local regions and the constructional information of whole image, the detected results are more precise, and the detected targets are more complete and homogeneous. The detecting precision of detection model is 0.65, the recall ratio is 0.8, F index is 0.73, and the indexes are higher than the indexes of other saliency detection methods based on superpixel. The CNN pre-training strategy for the specific detection task expends the data base of German traffic sign recognition benchmark(GTSRB), and fully uses the learning skills of CNN to learn the local fine detail features of object, so the recognition precision of CNN rises, and the recognition rate of CNN is 98.85% beyond the rate of SVM with 95.73%. 19 figs, 31 refs.

References:

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Last Update: 2016-10-20