Embedded AI System for Road Nail Detection

Abstract

Road safety and infrastructure maintenance are critical aspects of modern transportation systems to support mobility and protect road users. One major challenge is the presence of nails on roads, which can cause tire damage, traffic disruption, and accidents. This study proposes a road nail detection system using digital image processing constructed proceeding the You Only Look Once (YOLO)v4-tiny algorithm. The model demonstrated promising detection performance, with the loss value decreasing to 0.2876 and the mean Average Precision (mAP) reaching 70% at the 5400th iteration. Although a decline in mAP after this iteration indicated potential overfitting, the model was generally capable of recognizing nail objects within the training dataset. Performance evaluation showed an Average Precision (AP) of 90.87% for the “nail” class, with 394 true positives and 32 false positives, indicating strong detection capability. Additional metrics, including 85% precision, 82% F1-groove, also an average Intersection over Union (IoU) of 67.17%, indicate that the system performs reasonably well. The proposed system has potential applications in preventing tire punctures and improving road safety. Furthermore, this research potentially supports highway patrol officers in monitoring road conditions more efficiently by enabling early detection and rapid removal of hazardous objects such as nails.