[1]HE Ruibo,DI Lan,LIANG Jiuzhen.An improved deep learning algorithm for road traffic identification[J].CAAI Transactions on Intelligent Systems,2020,15(6):1121-1130.[doi:10.11992/tis.201811009]
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An improved deep learning algorithm for road traffic identification

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 6 Page number: 1121-1130 Column: 学术论文—机器学习 Public date: 2020-11-05
Title:
An improved deep learning algorithm for road traffic identification
Author(s):
HE Ruibo1; 2;  DI Lan1;  LIANG Jiuzhen3
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;
2. The 28th Research Institute of China Electronics Technology Group Corporation, Nanjing 210007, China;
3. School of Information Science and Engineering, Changzhou University, Changzhou 213164, China
Keywords:
road traffic identification; image segmentation; convolutional neural network; complex background elimination; data enhancement; normalization; squeeze-and-excitation network; residual connection
CLC:
TP391.4
DOI:
10.11992/tis.201811009
Abstract:
This study proposes a road traffic identification algorithm based on image preprocessing and deep-learning neural networks for complex environments. The proposed method uses not only the image segmentation technology but also the convolutional neural network model to more accurately identify the road traffic signs. First, a complete dataset is obtained via batch preprocessing operations, including illumination effect adjustment, complex background elimination, data enhancement, and normalization. Next, the convolutional neural network model is sufficiently trained based on the combination of the squeeze-and-excitation network and residual network structure concepts. Finally, the optimized network model is used to identify the road traffic signs. The experimental result shows that the proposed method reduces the training time by approximately 12% and that the recognition accuracy can reach 99.26%.
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