Supervised Contrastive Learning with Magnitude-Based Pruning for Arabic Traffic Sign Recognition

The rapid development of smart cities in the Arab region has accelerated the growth of Intelligent Transportation Systems (ITS), in which automatic traffic sign recognition plays an essential role in supporting road safety and intelligent vehicle applications. However, recognising Arabic traffic signs remains challenging due to the complexity of Arabic script. In addition, the limited computational and memory resources of automotive hardware highlight the need for compact and accurate recognition models. To address these challenges, this study proposes a framework that integrates supervised contrastive learning with magnitude-based pruning. Supervised contrastive learning enhances discriminative feature representations, while magnitude-based pruning introduces sparsity by removing low-magnitude weights. Four main configurations, namely the end-to-end ResNet50V2 baseline, the pruned baseline, the two-stage SCL model and the pruned SCL model, were evaluated across ten random seeds. The SCL model achieved an accuracy of 98.90 ± 0.50%, significantly higher than that of the end-to-end baseline (98.47 ± 0.28%; p=0.016, unadjusted). Magnitude-based pruning achieved 80% sparsity in the Dense-layer weights while preserving classification performance, with the pruned baseline and pruned SCL models achieving accuracies of 99.07% and 99.00%, respectively. Furthermore, pruning reduced the gzip-compressed model size by approximately 3.4%, whereas int8 quantisation reduced the model size from 99.35 to 25.48 MB. These findings indicate that the integration of SCL with magnitude-based pruning can maintain high recognition accuracy while introducing weight sparsity, highlighting its potential for future applications in ITS.