Hanlin Cai¹, Zheng Li¹, Jiaqi Hu¹, Wei Hong Lim², Sew Sun Tiang², Mastaneh Mokayef², Chin Hong Wong^1
1Maynooth International Engineering College, Fuzhou University, Fujian, China
²Faculty of Engineering, Technology and Built Environment, UCSI University, 1, Jalan Puncak Menara Gading, UCSI Heights, 56000 Cheras, Kuala Lumpur, Malaysia
pp. 80–88
ABSTRACT
This paper utilises image pre-processing techniques and deep residual neural networks to enhance the traffic sign detection system. A novel Analytic Hierarchy Process (AHP) model for performance evaluation has been proposed and utilised to determine the optimal parameter configuration of the learning models. Four evaluation metrics, namely accuracy, stability, response time, and system capability, have been defined for AHP measurements. The experiments were conducted using a comprehensive dataset, with VGG-16 and Google Net implemented for comparisons. Finally, the combination of ResNet-50 and the AHP model yielded the best results, achieving a 98.01% accuracy rate, 0.09% false alarm rate, and 1.28% undetection rate.

ARTICLE INFO
Article History
Received 05 December 2022
Accepted 07 October 2023

Keywords
Traffic sign detection system
Residual neural network (ResNet)
Analytic hierarchy process (AHP)

JAALR4204

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