Region-Adaptive Traffic Sign Detection using Enhanced YOLOv8 with Small-Object Optimization

Abstract

Traffic sign detection is an integral part of Advanced Driver Assistance Systems (ADAS) and self-driving cars wherein correct and timely detection helps ensure a safer driving experience. However, currently available models are trained on benchmark datasets that pertain to European or Chinese traffic scenarios. Such models lack the efficiency required for traffic scenarios in India due to multiple factors such as the variety of sign shapes and languages present, occlusions, varied light intensities and prevalence of smaller objects. In this paper, a region-based traffic sign detection system has been proposed using the enhanced YOLOv8 framework for Indian traffic scenarios. A subset of the Mapillary Traffic Sign Dataset (MTSD) has been curated, annotated and data-augmented with mosaic augmentation, change in brightness, adaptive scaling, and high-resolution training to overcome challenges posed by occlusions, varying light intensity and distant traffic signs.