| Title: | OS25-7 Deep Residual Neural Network for Efficient Traffic Sign Detection |
|---|---|
| Publication: | ICAROB2023 |
| Volume: | 28 |
| Pages: | 630-636 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2023.OS25-7 |
| Author(s): | Hanlin Cai, Zheng Li, Jiaqi Hu, Wei Hong Lim, Sew Sun Tiang, Mastaneh Mokayef, Chin Hong Wong |
| Publication Date: | February 9, 2023 |
| Keywords: | Traffic Sign Detection System, Residual Neural Network (RNN), Analytic Hierarchy Process (AHP) |
| Abstract: | This paper established three deep residual neural network models with different architectures for traffic sign detection. Also, a new systematic analytic hierarchy process method for model performance evaluation has been proposed, which was utilized to determine the configuration of the deep learning model. In this paper, four evaluation metrics were used for analytic hierarchy process measurement, they are accuracy, stability, response time, and system capability. Based on the Tsinghua-Tencent 100K dataset, experimental results verified the feasibility of the proposed models for traffic sign detection and recognition which has training and testing accuracy of 99.03% and 98.01% respectively. |
| PDF File: | https://alife-robotics.co.jp/members2023/icarob/data/html/data/OS/OS25/OS25-7.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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