A Region-specific Approach to Traffic Signrecognition in Kazakhstan: a Comparative Study Ofresnet-101, Mobilenetv2, and Yolov8
Year: 2024
Type: article
Abstract: This research addresses the critical need for accurate traffic sign recognition in Kazakhstan, which is essential for enhancing road safety anddeveloping advanced driver-assistance systems (ADAS). We created a comprehensive dataset tailored to Kazakhstan's traffic conditions and evaluated three state-of-the-art deep learning models: ResNet-101, MobileNetV2, andYOLOv8. Among these, YOLOv8 demonstrated superior performance, achieving 89.2% accuracy, 89.6% precision, 88.9% recall, and an 89.2% F1-score. This study highlights the effectiveness of tailored data augmentation techniques and the potential of YOLOv8 for real-time traffic sign recognition in dynamic environments, significantly contributing to the improvement of ADAS and road safety in Kazakhstan
Authors: Ulan Alsiyeu, Zhasdauren Duisebekov
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407221958