Real-time pothole detection using yolo models: an efficient and cost-effective solution for infrastructure monitoring

Abstract

Effective maintenance of roads is fundamental to transportation infrastructure with significant impact on safety and mobility. Notably, timely and precise pothole detection is essential for preventing road hazards and maintaining driving comfort. Traditional pothole detection methods are time- consuming and labor-intensive, prompting exploration of deep learning (DL)-based approaches for real-time and efficient pothole identification. In this study, three advanced “you only look once” (YOLO) models—YOLOv10n, YOLO11n, and YOLO11s—were evaluated on a dataset comprising of 13 767 images. Model performance was assessed using mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 (mAP@50). At this threshold, detections with an IoU ≥ 0.5 were considered true positives. In addition, precision, recall, and F 1-score (the harmonic mean of precision and recall) were also reported. Experimental results indicate that YOLO11n outperforms the other models, achieving a precision rate of 100%. Further-more, YOLO11n, with its compact size of 5.6 MiB and an inference time of 1.1 ms, demonstrated an optimal balance between detection accuracy and computational efficiency, making it suitable for real-time deployment. To enhance the system’s effectiveness, global positioning system (GPS) tagging using geographic information systems (GISs) was integrated for accurate pothole mapping, and depth cameras were utilized to improve detection reliability. The model was deployed on Streamlit Cloud with an intuitive interface that allows users to upload road videos with GPS data, detect potholes, visualize results on maps and heatmaps, and download processed outputs. Overall, the findings establish YOLO11n as a highly effective solution for automated pothole detection, providing a fast, scalable, and efficient tool for proactive road maintenance on edge networks. © 2026 Tongji University and Tongji University Press.