Image-based intelligent detection of typical defects of complex subway tunnel surface

We propose a new hybrid model algorithm that addresses the challenges of recognizing multiple objects. Specifically, objects are recognized from massive, large-scale, and complex tunnel images without repeated parameter adjustments and high-cost annotation datasets. Our algorithm utilizes a multi-sc...

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Vydáno v:Tunnelling and underground space technology Ročník 140; s. 105266
Hlavní autoři: Xu, Lizhi, Wang, Yaodong, Dong, Anqi, Zhu, Liqiang, Shi, Hongmei, Yu, Zujun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.10.2023
Témata:
Convolutional neural network
Image processing algorithm
Intelligent detection
Semantic segmentation
Structural health monitoring
Subway tunnel
Intelligent detection
Structural health monitoring
Semantic segmentation
Subway tunnel
Image processing algorithm
Convolutional neural network
ISSN:0886-7798
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Shrnutí:We propose a new hybrid model algorithm that addresses the challenges of recognizing multiple objects. Specifically, objects are recognized from massive, large-scale, and complex tunnel images without repeated parameter adjustments and high-cost annotation datasets. Our algorithm utilizes a multi-scale, fusion-based encoder–decoder segmentation model to classify objects from high-resolution images of the tunnel surfaces. To enhance the accuracy of crack identification from complex backgrounds, we incorporate the Expanded Threshold Search (ETS) algorithm and the Local Window Extraction (LWE) algorithm. The acquisition device and the algorithm, implementing the multi-object dataset, have successfully tested, whereby it recognizes five objects and attains the highest Intersection over Union (39.3% for the crack object, 65.6% for the leakage object, and 75.7% for the rest). [Display omitted] •An image acquisition device is designed for tunnel.•A multi-object dataset of complex subway tunnel images is efficiently built.•An efficient and accurate hybrid model is proposed.•Comprehensive experimental results are provided.
ISSN:0886-7798
DOI:10.1016/j.tust.2023.105266