Intelligent crack damage detection system in shield tunnel using combination of retinanet and optimal adaptive selection

With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2021-01, Vol.40 (3), p.4453-4469
Main Authors: Gao, Xin Wen, Li, ShuaiQing, Jin, Bang Yang, Hu, Min, Ding, Wei
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive systems
Algorithms
Complexity
Cracks
Damage detection
Field of view
Flaw detection
Image segmentation
Machine learning
Subway construction
Subways
Tunneling shields
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Summary:With the large-scale construction of urban subways, the detection of tunnel cracks becomes particularly important. Due to the complexity of the tunnel environment, it is difficult for traditional tunnel crack detection algorithms to detect and segment such cracks quickly and accurately. The article presents an optimal adaptive selection model (RetinaNet-AOS) based on deep learning RetinaNet for semantic segmentation on tunnel crack images quickly and accurately. The algorithm uses the ROI merge mask to obtain a minimum detection area of the crack in the field of view. A scorer is designed to measure the effect of ROI region segmentation to achieve optimal results, and further optimized with a multi-dimensional classifier. The algorithm is compared with the standard detection based on RetinaNet algorithm with an optimal adaptive selection based on RetinaNet algorithm for different crack types. The results show that our crack detection algorithm not only addresses interference due to mash cracks, slender cracks, and water stains but also the false detection rate decreases from 25.5–35.5% to about 3.6%. Meanwhile, the experimental results focus on the execution time to be calculated on the algorithm, FCN, PSPNet, UNet. The algorithm gives better performance in terms of time complexity.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-201296