Automatic Tunnel Crack Inspection Using an Efficient Mobile Imaging Module and a Lightweight CNN

Liao, Jianghai; Yue, Yan; Zhang, Dejin; Tu, Wei; Cao, Rui; Zou, Qin; Li, Qingquan

doi:10.1109/tits.2021.3138428

Cited by 36 publications

(23 citation statements)

References 54 publications

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“…In our future work, we will study two aspects. One is to use more advanced sensors for pavement modeling, and the other is to use more advanced machine learning methods such as deep convolution neural networks for crack detection in [55][56][57].…”

Section: Discussionmentioning

confidence: 99%

A Novel Path Voting Algorithm for Surface Crack Detection

Chen

Xiong

2022

Electronics

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Path voting is a widely used technique for line structure detection in images. Traditional path voting, based on minimal-path, is performed to track paths based on how seeds grow. The former requires to set a starting point and an end point. Thus, the performance of minimal-path path voting depends on the initialization. However, high-quality initialization often requires human interaction, which limits its applications in practice. In this paper, a fully automatic path voting method has been proposed and applied for crack detection. The proposed path voting is performed to segment images, which partitions an image patch along the potential crack path and integrates the path to form a crack probability map. After path voting, crack seeds are sampled and modeled into a graph, and the edge weights are assigned using an attraction field algorithm. Finally, cracks are extracted by using spanning tree and tree pruning algorithms. Experimental results demonstrate that the proposed path voting approach can effectively infer the cracks from 2D optic images and 3D depth images.

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Section: Discussionmentioning

confidence: 99%

A Novel Path Voting Algorithm for Surface Crack Detection

Chen

Xiong

2022

Electronics

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“…3 D reconstruction is an increasingly critical module in recent photogrammetric systems. It has been extensively utilized for constructing digital cities [1], documenting cultural heritages [2], and inspecting tunnel cracks [3], etc. 3D reconstruction can be implemented by using varying instruments, e.g., LiDAR (Light Detection and Ranging) scanners, TOF (Time of Flight) sensors, and optical cameras.…”

Section: Introductionmentioning

confidence: 99%

3D reconstruction of spherical images: A review of techniques, applications, and prospects

Li¹,

Weng²,

You³

et al. 2023

Preprint

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3D reconstruction plays an increasingly important role in modern photogrammetric systems. Conventional satellite or aerial-based remote sensing (RS) platforms can provide the necessary data sources for the 3D reconstruction of large-scale landforms and cities. Even with low-altitude UAVs (Unmanned Aerial Vehicles), 3D reconstruction in complicated situations, such as urban canyons and indoor scenes, is challenging due to frequent tracking failures between camera frames and high data collection costs. Recently, spherical images have been extensively used due to the capability of recording surrounding environments from one camera exposure. In contrast to perspective images with limited FOV (Field of View), spherical images can cover the whole scene with full horizontal and vertical FOV and facilitate camera tracking and data acquisition in these complex scenes. With the rapid evolution and extensive use of professional and consumergrade spherical cameras, spherical images show great potential for the 3D modeling of urban and indoor scenes. Classical 3D reconstruction pipelines, however, cannot be directly used for spherical images. Besides, there exist few software packages that are designed for the 3D reconstruction of spherical images. As a result, this research provides a thorough survey of the state-ofthe-art for 3D reconstruction of spherical images in terms of data acquisition, feature detection and matching, image orientation, and dense matching as well as presenting promising applications and discussing potential prospects. We anticipate that this study offers insightful clues to direct future research.

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“…Typically, cracks are among the early stage deterioration indicators that develop on the tunnel surface when the durability of the tunnel is diminished, and these must be primarily addressed in structural management. Tunnel cracks may lead to future leakage and damage, signifcantly degrading the durability of the tunnel [1,2]. In addition, they can be considerably afected by environmental changes [3] and can even cause accidents.…”

Section: Introductionmentioning

confidence: 99%

Learning Structure for Concrete Crack Detection Using Robust Super-Resolution with Generative Adversarial Network

Kim

Shim

Kang

et al. 2023

Structural Control and Health Monitoring

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To develop countermeasures against the inevitable aging of tunnels, accurate inspection is critical for ensuring stable tunnel services. Tunnels are large-scale infrastructures, whereas the cracks in such tunnels are small-scale objects, necessitating the development of methods capable of rapidly detecting cracks in a wide field of view. Therefore, this study proposes a new learning structure based on a segmentation network. This structure includes super-resolution and generative adversarial networks, which contribute to improved detection performance with robustness to input data. Furthermore, a method for the effective construction of training data for the application of the proposed structure is presented. Subsequently, the performance of the method over 1,606 crack images with randomly degraded qualities is evaluated. The proposed structure presents improved crack intersection over union and F1-scores of 63.686% and 77.811%, respectively, and low variances of 0.9008 and 0.5015 compared to the original structure. The results presented herein indicate the possible application of the proposed accurate condition inspection technology to tunnel maintenance in the future.

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Automatic Tunnel Crack Inspection Using an Efficient Mobile Imaging Module and a Lightweight CNN

Cited by 36 publications

References 54 publications

A Novel Path Voting Algorithm for Surface Crack Detection

A Novel Path Voting Algorithm for Surface Crack Detection

3D reconstruction of spherical images: A review of techniques, applications, and prospects

Learning Structure for Concrete Crack Detection Using Robust Super-Resolution with Generative Adversarial Network

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