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Automated Road Damage Detection using UAV Images and Deep Learning Techniques

2023Artigo científico Acesso aberto
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dc.contributor.authorLuís Augusto Silva
dc.contributor.authorValderi Reis Quietinho Leithardt
dc.contributor.authorVivian F. López Batista
dc.contributor.authorGabriel Villarrubia González
dc.contributor.authorJuan F. De Paz Santana
dc.date.accessioned2025-01-14T15:06:42Z
dc.date.available2025-01-14T15:06:42Z
dc.date.issued2023
dc.date.updated2023-06-26T11:01:45Z
dc.description.abstractThis 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/ACCESS.2023.3287770pt_PT
dc.identifier.slugcv-prod-3292390
dc.identifier.urihttp://hdl.handle.net/10400.26/53814
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectUAVpt_PT
dc.subjectRoad Damage Detectionpt_PT
dc.subjectDeep Learningpt_PT
dc.subjectObject-detectionpt_PT
dc.titleAutomated Road Damage Detection using UAV Images and Deep Learning Techniquespt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleIEEE Accesspt_PT
rcaap.cv.cienciaid0614-5834-E7F3 | Valderi Reis Quietinho Leithardt
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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