Abstract
Road traffic fatalities remain a significant global public health crisis, with the WHO identifying helmet and seatbelt non-compliance as among the leading contributing risk factors. According to the WHO Global Status Report on Road Safety 2023, Nigeria is part of WHO African region, which has the among the highest road traffic death rates in the world at 19.4 deaths per 100,000 people, with enforcement of helmet and seatbelt compliance remaining persistently limited by personnel constraints and the scale of the road network. With over 195,000 kilometers of road network to monitor, manual enforcement by Federal Road Safety Corps officers alone is not scalable, motivating the need for an automated, resource-efficient screening approach. This study fine-tunes a pre-trained YOLOv8n object detection model for helmet and seatbelt compliance detection using the publicly available SeatbeltHelmet dataset comprising 9,166 images across 9 annotated classes. The dataset was split into 8,020 training, 764 validation and 382 test images, and the model was trained for 50 epochs with a batch size of 16 and an image size of 640×640 pixels using the AdamW optimizer on a free-tier Google Colab GPU. The model was evaluated on a held-out test set of 382 images using mAP50, mAP50-95, Precision and Recall. The fine-tuned model achieved an overall mAP50 of 0.599, with Person-Seatbelt achieving the strongest per-class performance at mAP50 of 0.912 and Person-NoSeatbelt at 0.847. Helmet and No-Helmet detection performed comparatively weaker, at mAP50 of 0.537 and 0.380 respectively, reflecting the smaller object size and visual similarity of helmets at typical traffic camera distances. These results demonstrate that a lightweight fine-tuned object detection model is viable for automated road safety compliance screening in resource-constrained African settings where manual monitoring alone is insufficient to address the scale of the problem.