4.Optimizing Traffic Sign Detection System Using Deep Residual Neural Networks Combined with Analytic Hierarchy Process Model

Hanlin Cai1, Zheng Li1, Jiaqi Hu1, Wei Hong Lim2, Sew Sun Tiang2, Mastaneh Mokayef2, Chin Hong Wong1
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
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 History
Received 05 December 2022
Accepted 07 October 2023

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


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