Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network

Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact...

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Vydáno v:The international journal of pavement engineering Ročník 24; číslo 1
Hlavní autoři: Fakhri, Seyed Arya, Satari Abrovi, Mehran, Zakeri, Hamzeh, Safdarinezhad, Alireza, Fakhri, Arvin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 06.12.2023
Taylor & Francis LLC
Témata:
Algorithms
Artificial neural networks
CNN
Cracks
Damage
Datasets
deep learning
Encoders-Decoders
Machine learning
Neural networks
orthoimage
Pavement cracks
Pavement deterioration
Pavement management
pavement management system
Pavements
Photogrammetry
Roads & highways
UAV
Unmanned aerial vehicles
ISSN:1029-8436, 1477-268X
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Shrnutí:Crack detection on roads' surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it.
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ISSN:1029-8436
1477-268X
DOI:10.1080/10298436.2023.2255359