Revolutionizing Road Safety: Traffic Sign Detection in Bangladesh.

Abstract

Road safety remains a pressing global challenge, with over 1.3 million fatalities annually attributed to traffic accidents, a significant portion of which stem from misinterpretation or non-compliance with road signs. In Bangladesh, where over 4,000 deaths occur yearly due to road incidents, the inconsistent maintenance and standardization of traffic signage exacerbate this issue, particularly hindering the deployment of autonomous vehicles (AVs). This study presents the development and evaluation of a real-time traffic sign detection system using the YOLOv8 architecture, tailored to address the unique environmental and infrastructural conditions of Bangladesh. The system was trained on the yolobew dataset, achieving a mean average precision (mAP50-95) of 0.97266, a peak precision of 1.00 at maximum confidence, and a recall of 0.99872 at an optimal confidence threshold of 0.773, demonstrating robust performance across diverse sign classes. Comparative analysis with Faster R-CNN and RetinaNet highlights YOLOv8’s superior real-time inference speed of approximately 30 frames per second, alongside its efficiency in resource-constrained settings, despite challenges posed by class imbalance and occluded signs. The research further explores the influence of environmental factors such as weather and lighting on detection accuracy, identifying areas for improvement. Future directions include dataset expansion to mitigate class imbalance and enhancements in model efficiency through lightweight architectures and hybrid approaches. The implications of this work are profound, offering a foundation for integrating the system into AVs to enhance navigation safety, reduce human error, and support policy initiatives for standardized signage, thereby contributing to a significant reduction in road accident rates in Bangladesh as of August 2025.