A YOLOv8 Based Recognition System for Sri Lankan Road Traffic Signs

Abstract

Road traffic signs are crucial for road safety, providing drivers with essential information on speed limits, road conditions, and other important instructions. Accurate recognition of these signs is vital in preventing accidents and advancing autonomous driving systems. A major limitation in prior studies is the lack of models trained on large, customized datasets designed for Sri Lankan Context. This study addresses this gap by constructing a Sri Lankan Road traffic sign dataset and training a road traffic sign recognition system based on the You Only Look Once version 8 (YOLOv8) model using this dataset. The dataset was curated from images collected across Sri Lanka under diverse lighting and weather conditions, annotated, pre-processed, and further expanded through data augmentation. The dataset consists of 6,000 images in total spanning across 37 Sri Lankan Road traffic sign classes. The YOLOv8 model trained on this dataset achieved a mean Average Precision (mAP) of 91.1% at an IoU threshold of 0.5, with precision above 85% and balanced F1-scores above 86%. The model performed robustly across varied conditions, excelling in common signs while showing reduced performance for rare or visually similar classes. Overall, this work introduces a large-scale Sri Lankan Road traffic sign dataset and a road traffic sign recognition model that demonstrates superior accuracy compared to prior studies, highlighting the importance of localized datasets and modern deep learning approaches in improving traffic safety and intelligent transportation systems.