Abstract
There is a significant safety and infrastructure challenge in resource-constrained regions like Nigeria and Africa posed by damaged roads and potholes. Over 70% of Nigeria's paved roads are damaged, and this contributes to road accidents and high vehicle repair costs. The current method of road inspection is manual, which is slow and reactive rather than proactive, making an automated detection system necessary. Deep learning models can scan large numbers of road images quickly and mark exactly where the damage is, which helps road agencies decide which roads to fix first. This study fine-tunes a pre-trained YOLOv8n object detection model on a publicly available pothole dataset comprising 665 images to automatically detect and localize road damage from images. The model was trained for 50 epochs using a free cloud GPU, with the dataset split into training, validation, and test sets. Data augmentation techniques such as flipping, rotating, and brightness adjustment were also applied to improve the model's ability to handle different road conditions. The model achieved an mAP@0.5 of 78.0%, precision of 81.4%, and recall of 67.5%, outperforming the dataset baseline of 74.0%. These results show that the model can correctly detect most potholes while keeping false detections low, though about one in three potholes was still missed, meaning the model works best as a support tool alongside human inspectors rather than a full replacement. These results also demonstrate that fine-tuning a lightweight pre-trained model on free cloud hardware represents a viable and accessible approach to automated road infrastructure monitoring in resource-constrained African settings.