Citation

Alqahtani, Ali and Alqaysi, Eyad and Alsarhani, Waleed and Lee, Poo and Alsharafi, Abdulmajeed and Alzahrani, Mohammed and Almosa, Abdulrahman and Osten, Wolfgang (2025) MobileViTv2-MLP: Arabic traffic sign recognition with enhanced lightweight vision transformers. In: Proceedings Volume 13517, Seventeenth International Conference on Machine Vision (ICMV 2024); 135171H (2025), 24 February 2025, Edinburg, United Kingdom.

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Abstract

Traffic Sign Recognition (TSR) plays a pivotal role in advanced driver-assistance systems (ADAS) and autonomous vehicles, serving as a fundamental element for safe and efficient road navigation. Due to a diverse array of factors such as variations in sign appearance, fluctuating lighting conditions, occlusions, and environmental influences, the task of TSR poses significant challenges. These challenges have been addressed with advanced deep learning techniques, particularly with Convolutional Neural Networks (CNNs) and other architectures proficient in handling visual data. While such approaches have shown promise, they are often limited in their ability to effectively manage the aforementioned challenges. To overcome these obstacles, we propose a novel approach called the MobileViTv2-MLP method. This approach aims to develop a resilient model specifically tailored to the task of identifying and categorizing traffic signs encountered during driving. Our methodology involves training the model on an extensive dataset comprising diverse representations of traffic signs under varying conditions, ensuring its adaptability and reliability in real-world scenarios. Through experimentation and evaluation, our refined approach has demonstrated exceptional accuracy, achieving a remarkable 99.75%. This outcome underscores the robustness and effectiveness of our proposed method in addressing the challenges inherent in TSR, thereby contributing to the advancement of intelligent transportation systems.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QC Physics > QC350-467 Optics. Light
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	30 Apr 2025 06:05
Last Modified:	30 Apr 2025 06:05
URII:	http://shdl.mmu.edu.my/id/eprint/13731

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