Automated Road Damage Detection Using UAVs and Deep Learning
A Scalable Solution for Infrastructure Maintenance
Tuğba Yıldızlı (TU Delft - Water Systems Engineering)
Merve Bayraktar (Technische Hochschule Augsburg)
Burcu Güldür Erkal (Technische Hochschule Augsburg)
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Abstract
This study presents an automated pavement surface inspection approach using UAV imagery and the YOLOv7 deep learning model. The aim is to detect common surface defects such as longitudinal cracks, reflective cracks, alligator cracks, and potholes with high accuracy and efficiency. The system was trained on publicly available datasets and tested with aerial images captured at Hacettepe University’s Beytepe Campus. Evaluation results demonstrated a precision of 0.51, recall of 0.45, and mean Average Precision (mAP@0.5) of 0.42. These findings confirm the feasibility of integrating UAV platforms and deep learning for road defect detection. Unlike traditional methods, which are time-consuming and labor-intensive, this approach enables faster, scalable, and cost-effective inspections. The proposed framework contributes to safer and more sustainable road infrastructure maintenance by facilitating proactive monitoring and reducing operational burdens. Its ability to analyze large areas in real time makes it particularly suitable for modern transportation networks and smart city applications.
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