Abstract: Traffic sign detection is a key task in intelligent transportation systems, supporting road safety and traffic flow. This study introduces RoadNet, a lightweight Convolutional Neural Network (CNN) designed for real-time detection and classification of traffic signs in Moroccan road environments. The system addresses challenges such as occlusion, illumination variability, and diverse sign structures. Built on deep learning techniques, RoadNet leverages multiscale feature extraction and transfer learning to improve detection accuracy and generaliza-tion. The dataset includes four sign categories: speed limit, stop, crosswalk, and traffic light. Extensive image preprocessing and augmentation were applied to increase robustness. Results show that RoadNet outperforms baseline models like VGG16, achieving 96% training accuracy and 88.6% validation accuracy, with superior precision, recall, and F1-score. The model maintains low loss and performs reliably under constrained resources. This research confirms the effectiveness of CNN-based architectures for traffic sign detection in real-world Moroccan settings. It contributes to the deployment of AI-powered solutions for smart mobility and logistics, especially in regions with limited computational resources.

OUAHBI Younesse and ZITI Soumia, “Advancing Traffic Sign Detection with Convolutional Neural Networks: A Deep Learning Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160693

@article{Younesse2025,
title = {Advancing Traffic Sign Detection with Convolutional Neural Networks: A Deep Learning Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160693},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160693},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {OUAHBI Younesse and ZITI Soumia}
}