International Journal of Electrical Engineering and Computer Science
E-ISSN: 2769-2507
Volume 7, 2025
Optimizing Traffic Sign Recognition for Malaysian Roads Using Deep Learning Models
Authors: , , ,
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Abstract: This study evaluates models for traffic sign detection and classification. Initially, LeNet was trained from scratch on the GTSRB dataset, achieving 98.74% training accuracy and 86.54% test accuracy. In the second phase, pre-trained models like DenseNet201 and Xception were fine-tuned, with DenseNet201 achieving 100% test accuracy and Xception 95%. DenseNet201's performance was best with a batch size of 20, and the Adam optimizer yielded the highest performance. The fully trained DenseNet201 model was integrated with a laptop camera for real-time detection, classifying images within 2 seconds. The study highlights the potential of deep learning models for real-time traffic sign detection and addresses challenges like overfitting and dataset limitations.
Pages: 90-96
DOI: 10.37394/232027.2025.7.9