Abstract
Road traffic accidents remain a leading cause of injury and death worldwide, and driver failure to correctly identify traffic signs is a significant contributing factor. Many devastating and deadly road traffic incidents are directly linked to poor sign recognition, particularly in Nigerian and developing-region road conditions, where faded signage, poor lighting, and occlusion are common conditions poorly served by existing standardized-signage-focused research. Most existing work is either classification-only on pre-cropped signs, such as NaijaTrafficNet, or detection-based but not comparatively benchmarked for resource-constrained deployment relevant to this context. This study aims to determine which YOLOv8 variant offers the best accuracy-speed-size tradeoff for real-time detection under resource-constrained conditions relevant to developing-region deployment. This study addresses this gap by comparatively benchmarking three YOLOv8 variants — nano, small, and medium on an 11-class filtered traffic sign detection dataset, comprising 800 training, 223 validation, and 79 held-out test images, evaluated on precision, recall, mAP@0.5, mAP@0.5:0.95, FPS, and model size on a held-out test set. All variants performed closely within a narrow accuracy band, with YOLOv8n matching or exceeding the larger variants in accuracy while offering the smallest footprint and highest inference speed, showing that higher model capacity does not improve generalization at this dataset scale. YOLOv8n achieved the highest test-set mAP@0.5 of 0.897 at only 3.0M parameters and 6.2MB, versus YOLOv8m's 0.886 at 25.8M parameters and 52.0MB — despite YOLOv8m ranking highest on validation, underscoring the importance of held-out test evaluation. YOLOv8n is therefore recommended as the most viable variant for real-time, resource-constrained deployment on dashcams or roadside cameras in developing-region contexts, pending real-world Nigerian and embedded-hardware validation.