A REGION-SPECIFIC APPROACH TO TRAFFIC SIGNRECOGNITION IN KAZAKHSTAN: A COMPARATIVE STUDY OFRESNET-101, MOBILENETV2, AND YOLOV8

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