Automated bridge crack evaluation through deep super resolution network-based hybrid image matching

Jang, Keunyoung; Jung, Hyunjun; An, Yun‐Kyu

doi:10.1016/j.autcon.2022.104229

Cited by 30 publications

(9 citation statements)

References 48 publications

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“…Furthermore, the visual and thermal images are combined in order to improve the segmentation accuracy. 103 Jang et al 104 proposed a deep super-resolution segmentation network for assessing bridge cracks. The combination of the two types of images allows a precise evaluation of cracks at the 100 μm.…”

Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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“…NDI inspection methodologies employed by researchers for the inspection of thick structures/concrete comprise computer-vision-based visual inspection [ 5 , 6 , 7 , 8 , 9 ] and physics-based NDI techniques, such as ultrasonics [ 10 , 11 , 12 , 13 ], radiography [ 14 ], and thermography [ 15 , 16 , 17 , 18 ]. Although physics-based NDI techniques are promising for the diagnosis of defects at the coupon level, the complex necessity of tailor-made auxiliary devices for measurement and scalability for in situ field measurements remains questionable.…”

Section: Introductionmentioning

confidence: 99%

“…The continuously evolving semiconductor industry affords consumer-grade inexpensive vision-based sensors to NDI inspectors. Computer-vision-based inspection enables rapid large-area measurement scalability with image sensors that are easily configurable with robotic systems [ 17 , 19 , 20 , 21 , 22 , 23 ] or unmanned aerial vehicles (UAVs) [ 24 , 25 , 26 ]. Multiple types of autonomous robotic systems, such as ring-type climbing robots, camera-equipped mobile robots, and movable fixtures with multiple degrees of freedom, have recently been employed to explore concrete structures and assist in maintenance [ 17 , 19 , 20 , 21 , 22 , 23 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…Computer-vision-based inspection enables rapid large-area measurement scalability with image sensors that are easily configurable with robotic systems [ 17 , 19 , 20 , 21 , 22 , 23 ] or unmanned aerial vehicles (UAVs) [ 24 , 25 , 26 ]. Multiple types of autonomous robotic systems, such as ring-type climbing robots, camera-equipped mobile robots, and movable fixtures with multiple degrees of freedom, have recently been employed to explore concrete structures and assist in maintenance [ 17 , 19 , 20 , 21 , 22 , 23 , 27 ]. Although robotic-platform-based inspection is a promising way forward for automation and scalability, the following limitations exist: (i) customized systems or auxiliary support systems are required for different structural components and this is often unsuitable for inaccessible surfaces; (ii) robotic platforms often have a short working distance from the target structure to achieve sufficient image resolution for reliable crack detection, i.e., a small field-of-view (FOV), which limits the range of coverage over a specified inspection duration; (iii) although the dimensions of the crack can be accurately identified in image coordinates, a lack of reference or calibration targets within a small FOV results in an erroneous estimation of the true dimensions of the crack in world coordinates; (iv) regarding bridge components, robotic platforms are difficult to operate without additional surface preparation due to the continuous exposure to degrading environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification

Geetha

Yang

Sim

2023

Sensors

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Existing deep learning (DL) models can detect wider or thicker segments of cracks that occupy multiple pixels in the width direction, but fail to distinguish the thin tail shallow segment or propagating crack occupying fewer pixels. Therefore, in this study, we proposed a scheme for tracking missing thin/propagating crack segments during DL-based crack identification on concrete surfaces in a computationally efficient manner. The proposed scheme employs image processing as a preprocessor and a postprocessor for a 1D DL model. Image-processing-assisted DL as a precursor to DL eliminates labor-intensive labeling and the plane structural background without any distinguishable features during DL training and testing; the model identifies potential crack candidate regions. Iterative differential sliding-window-based local image processing as a postprocessor to DL tracks missing thin cracks on segments classified as cracks. The capability of the proposed method is demonstrated on low-resolution images with cracks of single-pixel width, captured using unmanned aerial vehicles on concrete structures with different surface textures, different scenes with complicated disturbances, and optical variability. Due to the multi-threshold-based image processing, the overall approach is invariant to the choice of initial sensitivity parameters, hyperparameters, and the sequence of neuron arrangement. Further, this technique is a computationally efficient alternative to semantic segmentation that results in pixelated mapping/classification of thin crack regimes, which requires labor-intensive and skilled labeling.

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“…Bae et al (2021) proposed a novel DL-based SR crack network named SrcNet, which can be used to quadruple the image resolution with very little noise, thus improving computer vision-based automated crack detection capability by 24%. On the basis of this work, Jang et al (2022) subsequently proposed an improved automated bridge crack assessment technique based on deep SR networks through hybrid image matching, which can be used to combine vision and laser-induced infrared (IR) thermal imaging images for the sophisticated evaluation of the 100 μm-level cracks. With inspiration from those two works, Xiang et al (2022) proposed an automatic detection method for tiny cracks based on SR reconstruction (SRR), which can be used to successfully solve the problem that the crack images collected by the unmanned aerial vehicle (UAV) are difficult to effectively use due to motion blur and insufficient camera resolution.…”

Section: Introductionmentioning

confidence: 99%

Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution images

Chu

Long

Guo

et al. 2023

Computer aided Civil Eng

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High‐resolution (HR) crack images offer more detailed information for evaluating the structural condition and formulating effective maintenance or rehabilitation plans. However, the meticulous segmentation of HR crack images has been a challenge due to the limitations of mainstream deep learning algorithms that extract features in a discrete manner, as well as the constraints of computing resources. To address this issue, a novel implicit function‐integrated architecture, called the crack continuous refinement network (CCRN), was proposed for meticulous segmentation of cracks from HR images using a continuous representation manner. First, a crack continuous alignment module with a position encoding function was proposed to encode the tiny crack pixels that are easily lost in the sampling process. Then, a lightweight decoder embedded with implicit functions was customized to recover crack details from the aligned latent features and continuous position encoding information. Afterward, the gap between low‐resolution training images and HR inference results was bridged by the proposed continuous inference strategy. Finally, the robustness and practicability of the well‐trained CCRN were demonstrated by a parallel comparison and an unmanned aerial vehicle‐based field experiment.

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Automated bridge crack evaluation through deep super resolution network-based hybrid image matching

Cited by 30 publications

References 48 publications

Review of artificial intelligence-based bridge damage detection

Review of artificial intelligence-based bridge damage detection

Fast Detection of Missing Thin Propagating Cracks during Deep-Learning-Based Concrete Crack/Non-Crack Classification

Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution images

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