Enhancing Traffic Sign Recognition in Rough Weather to Reduce Traffic Accidents

Authors

DOI:

https://doi.org/10.21015/vtse.v13i4.2306

Abstract


Background: Automated traffic sign recognition is a technology that helps in detecting and understanding the traffic signs on the road. These signs may include stop signs, speed limits and some other warning signs. These signs are important in maintaining road safety and helping drivers obeying the traffic rules. Machine learning and computer vision together have advanced in this field and many methods have been proposed for automated traffic sign detection and recognition. Despite these advancements, there are still many challenging situations including occlusion, varying lighting conditions, difficult environmental conditions and sign variations which still need attention. Methods: We use Arabic traffic sign dataset to train EfficientNetB3 architecture with attention mechanism to classify the traffic sign under the unique visual and linguistic complexities as well as diverse environmental conditions. We also improved the dataset by augmenting and adding extra images to cover actual scenarios like fog, heavy rain, low light etc. making it stronger for testing and future research. Results: Our trained model achieved the accuracy of 99.61\% in testing better than the compared to the baseline CNN-Resnet model. Conclusion: In this research we addressed the existing limitations and sets a benchmark for the effective and efficient classification of Arabic traffic signs, particularly in challenging weather conditions.

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Published

2025-12-31

How to Cite

Ayazuddin, R., Alqahtan, H., Amin, N. U., Hussain, M., & Koondhar, M. Y. (2025). Enhancing Traffic Sign Recognition in Rough Weather to Reduce Traffic Accidents. VFAST Transactions on Software Engineering, 13(4), 149–158. https://doi.org/10.21015/vtse.v13i4.2306

Issue

Vol. 13 No. 4 (2025): October-December

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Articles

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