Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Shim, Seungbo; Kim, Jin; Cho, Gye-Chun; Lee, Seong-Won

doi:10.1109/access.2020.3022786

Cited by 46 publications

(37 citation statements)

References 35 publications

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“…Field tests in multiple tunnels prove the capability of clearly achieving the acquisition of tunnel lining images. Aided by related algorithms for automatic identification and segmentation of cracks and extraction of crack geometric parameters [18,22,23], which have been extensively studied, the mechanized and intelligent operation for the whole-process tunnel crack collection, analysis, and statistics can be realized.…”

Section: Discussionmentioning

confidence: 99%

Design of Fast Acquisition System and Analysis of Geometric Feature for Highway Tunnel Lining Cracks Based on Machine Vision

Wang

Zhai

et al. 2022

Applied Sciences

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Under the dual effects of the rapid growth of tunnel mileage and operating years, the application and research of tunnel crack identification based on machine vision are increasing with the vigorous development of machine vision. However, due to the complex environment in tunnels, it is difficult to quickly obtain tunnel lining cracks via computer visions in the tunnel. Therefore, this paper presents the design of a fast acquisition system with the geometric feature analysis for tunnel lining cracks, which has been integrated into a tunnel fast inspection vehicle with a machine vision module. Through the research on the image acquisition system of the tunnel lining, the parameter selection of the crack shooting hardware system is determined, and the fast calculation method of shooting parameters is proposed. The geometric characteristic analysis of the tunnel lining crack image is employed to calculate crack width and determine the optimal gray value of crack extraction. Field tests have been conducted in the highway tunnels in Zhejiang and Yunnan provinces in China and the result indicates that the proposed approach yields much better performance in the detection efficiency, whose time of detection is only 1%, and the number of personnel required is only 40% of the traditional pure manual method. Compared with similar systems, it also has significant advantages in crack resolution and detection speed. This research provides a means of rapid acquisition of tunnel cracks and laying a foundation for the evaluation of the service performance of the tunnel.

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Section: Discussionmentioning

confidence: 99%

Design of Fast Acquisition System and Analysis of Geometric Feature for Highway Tunnel Lining Cracks Based on Machine Vision

Wang

Zhai

et al. 2022

Applied Sciences

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“…Jenkins et al (2018) Xu et al (2019) for crack identification in the steel box girder of bridges containing complicated disturbing background and handwriting. An adversarial learning architecture of Semi-supervised was proposed by Li et al (2020) and applied for pavement crack detection and by Shim et al (2020) for Crack Detection in Concrete Structures.…”

Section: Methodology and Logicmentioning

confidence: 99%

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Elghaish

Matarneh

Talebi

et al. 2021

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Purpose The massive number of pavements and buildings coupled with the limited inspection resources, both monetary and human, to detect distresses and recommend maintenance actions lead to rapid deterioration, decreased service life, lower level of service and increased community disruption. Therefore, this paper aims at providing a state-of-the-art review of the literature with respect to deep learning techniques for detecting distress in both pavements and buildings; research advancements per asset/structure type; and future recommendations in deep learning applications for distress detection. Design/methodology/approach A critical analysis was conducted on 181 papers of deep learning-based cracks detection. A structured analysis was adopted so that major articles were analyzed according to their focus of study, used methods, findings and limitations. Findings The utilization of deep learning to detect pavement cracks is advanced compared to assess and evaluate the structural health of buildings. There is a need for studies that compare different convolutional neural network models to foster the development of an integrated solution that considers the data collection method. Further research is required to examine the setup, implementation and running costs, frequency of capturing data and deep learning tool. In conclusion, the future of applying deep learning algorithms in lieu of manual inspection for detecting distresses has shown promising results. Practical implications The availability of previous research and the required improvements in the proposed computational tools and models (e.g. artificial intelligence, deep learning, etc.) are triggering researchers and practitioners to enhance the distresses’ inspection process and make better use of their limited resources. Originality/value A critical and structured analysis of deep learning-based crack detection for pavement and buildings is conducted for the first time to enable novice researchers to highlight the knowledge gap in each article, as well as building a knowledge base from the findings of other research to support developing future workable solutions.

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“…A penalty term was introduced to modify the traditional loss function of the GAN network to improve overall feature extraction and enhance the quality of the generated thermographic image when compared with traditional GANs. A multiscale segmentation neural network, discriminator neural network, and adversarial learning technique for the pixel‐level detection of cracks located on concrete surfaces were proposed by Shim et al 27 The introduction of dense layers improved the learning performance of the segmentation network and reduced the amount of labeled ground truth data used during training.…”

Section: Introductionmentioning

confidence: 99%

Multiclass damage detection in concrete structures using a transfer learning‐based generative adversarial networks

Dunphy

Sadhu

Wang

2022

Structural Contr & Hlth

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A large amount of the world's existing infrastructure is reaching the end of its service life, requiring intervention in the form of structural rehabilitation or replacement. A critical aspect of such asset management is the condition assessment of these structures to evaluate their existing health and dictate the scheduling and extent of required rehabilitation. It has been demonstrated that human-based manual inspections face logistical constraints and are expensive, time extensive, and subjective, depending on the knowledge of the inspection. Recently, autonomous vision-based techniques have been proposed as an alternative, more accurate method for the inspection of deteriorating structures. Convolutional neural networks (CNNs) have demonstrated state-of-the-art accuracy with respect to damage classification for concrete structures and are often implemented to process images taken from vision-based sensors such as cameras, smartphones, and drones. However, these archetypes require a large database of annotated images to train the network to an accurate level, which is not readily available for real-life structures. Moreover, CNNs are limited to the extent by which they are trained; they are often only trained for binary damage classification of a singular material model. This paper addresses these challenges of CNNs through the application of a generative adversarial network (GANs) for multiclass damage detection of concrete structures. The proposed GAN is trained using the SDNET2018 dataset to detect cracking, spalling, pitting, and construction joints in concrete surfaces. Moreover, transfer learning is implemented to transfer the learned features of the GAN to a CNN architecture to allow for accurate image classification. It is concluded that, for a 0%-30% reduction in the amount of labeled data used, the proposed GAN method has comparable accuracy to traditional CNNs.

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Multiscale and Adversarial Learning-Based Semi-Supervised Semantic Segmentation Approach for Crack Detection in Concrete Structures

Cited by 46 publications

References 35 publications

Design of Fast Acquisition System and Analysis of Geometric Feature for Highway Tunnel Lining Cracks Based on Machine Vision

Design of Fast Acquisition System and Analysis of Geometric Feature for Highway Tunnel Lining Cracks Based on Machine Vision

Deep learning for detecting distresses in buildings and pavements: a critical gap analysis

Multiclass damage detection in concrete structures using a transfer learning‐based generative adversarial networks

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