Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges

Bae, Hyun‐Jin; Jang, Keunyoung; An, Yun‐Kyu

doi:10.1177/1475921720917227

Cited by 61 publications

(25 citation statements)

References 48 publications

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“…The overall distribution of the articles is shown in Figure 9 and the articles are shown in Table 10. [24,35,83,97,98,101,164,168,170,188,197,200,[205][206][207][208][209]…”

Section: Dataset Based Analysismentioning

confidence: 99%

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Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

et al. 2022

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The use of deep learning (DL) in civil inspection, especially in crack detection, has increased over the past years to ensure long-term structural safety and integrity. To achieve a better understanding of the research work on crack detection using DL approaches, this paper aims to provide a bibliometric analysis and review of the current literature on DL-based crack detection published between 2010 and 2022. The search from Web of Science (WoS) and Scopus, two widely accepted bibliographic databases, resulted in 165 articles published in top journals and conferences, showing the rapid increase in publications in this area since 2018. The evolution and state-of-the-art approaches to crack detection using deep learning are reviewed and analyzed based on datasets, network architecture, domain, and performance of each study. Overall, this review article stands as a reference for researchers working in the field of crack detection using deep learning techniques to achieve optimal precision and computational efficiency performance in light of electing the most effective combination of dataset characteristics and network architecture for each domain. Finally, the challenges, gaps, and future directions are provided to researchers to explore various solutions pertaining to (a) automatic recognition of crack type and severity, (b) dataset availability and suitability, (c) efficient data preprocessing techniques, (d) automatic labeling approaches for crack detection, (e) parameter tuning and optimization, (f) using 3D images and data fusion, (g) real-time crack detection, and (h) increasing segmentation accuracy at the pixel level.

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“…The overall distribution of the articles is shown in Figure 9 and the articles are shown in Table 10. [24,35,83,97,98,101,164,168,170,188,197,200,[205][206][207][208][209]…”

Section: Dataset Based Analysismentioning

confidence: 99%

“…Custom and Public Concrete and Pavement [24,35,83,97,98,101,164,168,170,188,197,200,[205][206][207][208][209]…”

Section: Dataset Type Domain Workmentioning

confidence: 99%

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

et al. 2022

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“…Effective maintenance of infrastructure is possible through the integration of BIM with state-of-the-art technologies, such as unmanned robots and Unmanned Aerial Vehicle (UAV) systems with digital cameras or 3D Laser Scanning and Light Detection and Ranging (LiDAR), 3D model construction algorithms, and artificial intelligence algorithms, etc. [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Infrastructure BIM Platform for Lifecycle Management

Jang

Kim²,

et al. 2021

Applied Sciences

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Recently, the application of the BIM technique to infrastructure lifecycle management has increased rapidly to improve the efficiency of infrastructure management systems. Research on the lifecycle management of infrastructure, from planning and design to construction and management, has been carried out. Therefore, a systematic review of the literature on recent research is performed to analyze the current state of the BIM technique. State-of-the-art techniques for infrastructure lifecycle management, such as unmanned robots, sensors and processing techniques, artificial intelligence, etc., are also reviewed. An infrastructure BIM platform framework composed of BIM and state-of-the-art techniques is then proposed. The proposed platform is a web-based platform that contains quantity, schedule (4D), and cost (5D) construction management, and the monitoring systems enable collaboration with stakeholders in a Common Data Environment (CDE). The lifecycle management methodology, after infrastructure construction, is then completed and is developed using state-of-the-art techniques using unmanned robots, scan-to-BIM, and deep learning networks, etc. It is confirmed that collaboration with stakeholders in the CDE in construction management is possible using an infrastructure BIM platform. Moreover, lifecycle management of infrastructure is possible by systematic management, such as time history analysis, damage growth prediction, decision of repair and demolition, etc., using a regular inspection database based on an infrastructure BIM platform.

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“…Monitoring the integrity of aging structures has become increasingly important in terms of extending structures' service life and saving maintenance costs. For effective monitoring of large-scale structures, unmanned aerial vehicles (UAVs) have recently played a key role, in that faster and safer inspection is possible than expert-dependent visual inspection, even for inaccessible areas by human beings [1][2][3]. One of the most popular UAV-based inspection strategies is that structural exterior damage can be effectively assessed by using UAV-captured digital images.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching

Kang

2021

Applied Sciences

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This paper presents a deep learning-based automated background removal technique for structural exterior image stitching. In order to establish an exterior damage map of a structure using an unmanned aerial vehicle (UAV), a close-up vision scanning is typically required. However, unwanted background objects are often captured within the scanned digital images. Since the unnecessary background objects often cause serious distortion on the image stitching process, they should be removed. In this paper, the automated background removal technique using deep learning-based depth estimation is proposed. Based on the fact that the region of interest has closer working distance than the background ones from the camera, the background region within the digital images can be automatically removed using a deep learning-based depth estimation network. In addition, an optimal digital image selection based on feature matching-based overlap ratio is proposed. The proposed technique is experimentally validated using UAV-scanned digital images acquired from an in-situ high-rise building structure. The validation test results show that the optimal digital images obtained from the proposed technique produce the precise structural exterior map with computational cost reduction of 85.7%, while raw scanned digital images fail to construct the structural exterior map and cause serious stitching distortion.

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Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges

Cited by 61 publications

References 48 publications

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

Bibliometric Analysis and Review of Deep Learning-Based Crack Detection Literature Published between 2010 and 2022

Infrastructure BIM Platform for Lifecycle Management

Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching

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