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Automated Road Damage Detection using UAV Images and Deep Learning Techniques

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Summary:This paper presents a novel automated road damage detection approach using Unmanned Aerial Vehicle (UAV) images and deep learning techniques. Maintaining road infrastructure is critical for ensuring a safe and sustainable transportation system. However, the manual collection of road damage data can be labor-intensive and unsafe for humans. Therefore, we propose using UAVs and Artificial Intelligence (AI) technologies to improve road damage detection’s efficiency and accuracy significantly. Our proposed approach utilizes three algorithms, YOLOv4, YOLOv5, and YOLOv7, for object detection and localization in UAV images. We trained and tested these algorithms using a combination of the RDD2022 dataset from China and a Spanish road dataset. The experimental results demonstrate that our approach is efficient and achieves 59.9% mean average precision mAP@.5 for the YOLOv5 version, 73.20% mAP@.5 for the YOLOv7 version, and 65.70% mAP@.5 for a YOLOv5 model with a Transformer Prediction Head. These results demonstrate the potential of using UAVs and deep learning for automated road damage detection and pave the way for future research in this field.
Main Authors:Luís Augusto Silva
Other Authors:Valderi Reis Quietinho Leithardt; Vivian F. López Batista; Gabriel Villarrubia González; Juan F. De Paz Santana
Subject:UAV Road Damage Detection Deep Learning Object-detection
Year:2023
Country:Portugal
Document type:article
Access type:open access
Associated institution:Instituto Politécnico de Portalegre
Language:English
Origin:Instituto Politécnico de Portalegre

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