Comparison of Different Models for Traffic Signs Under Weather Conditions Using Image Detection and Classification

This study focuses on enhancing the accuracy of traffic sign detection systems for self-driving. With the increasing proliferation of autonomous vehicles, reliable detection and interpretation of traffic signs is crucial for road safety and efficiency. The primary goal of this research was to improve the performance of traffic sign detection, particularly in identifying unfamiliar signs and dealing with adverse weather conditions. We obtained a dataset of 3,480 images from Roboflow and utilized deep learning techniques, including Convolutional Neural Networks (CNNs) and algorithms such as YOLO and the Vision Engineering (VGG) toolkit. Unlike previous studies that focused on a single version of YOLO, this study conducted a comparative analysis of different deep-learning models, including YOLOv5, YOLOv8, and VGG-16. The study results show promising outcomes, with YOLOv5 achieving an accuracy of up to 94.2%, YOLOv8 reaching 95.3% accuracy, and VGG-16 outperforming the other techniques with an impressive 98.68% accuracy. These findings highlight the significant potential for future advancements in traffic sign detection systems, contributing to the ongoing efforts to enhance the safety and efficiency of autonomous driving technologies.

@article{Alshahrani2024,
  title     = {Comparison of Different Models for Traffic Signs Under Weather Conditions Using Image Detection and Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Amal Alshahrani and Leen Alshrif and Fatima Bajawi and Razan Alqarni and Reem Alharthi and Haneen Alkurbi},
  doi       = {10.14569/IJACSA.2024.0150736},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150736}
}