A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level

As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network...

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Vydáno v:Computer-aided civil and infrastructure engineering Ročník 37; číslo 13; s. 1721 - 1736
Hlavní autoři: Chen, Jun, He, Ye
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.11.2022
Témata:
Coders
Crack geometry
Cracks
Decoding
Flaw detection
Neural networks
Pavement condition
Pixels
ISSN:1093-9687, 1467-8667
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Shrnutí:As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network for the detection and evaluation of road cracks at pixel level, which combines the advantage of encoding–decoding network and attention mechanism and can thus extract the crack pixels more accurately and efficiently. The proposed network achieves an excellent detection performance with IOU = 92.85%, precision = 96.90%, recall = 95.36%, F1 = 95.53%. Compared with the other advanced networks, the accuracy of the proposed method is substantially enhanced. The quantitative estimation of key geometrical features of cracks including length, width, and area is successfully realized with the development of a prototype of an intelligent mobile system. Compared with the ground truth, the maximum crack width shows the lowest relative error rate, which ranges −31.75%∼28.57%.
Bibliografie:Funding information
National Key Research and Development Program of China, Grant/Award Number: 2018YFB1600200; National Natural Science Foundation of China, Grant/Award Number: 51978027.
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ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12826