A vision-based approach for autonomous crack width measurement with flexible kernel

Jin, Suyeong; Lee, Sang Eon; Hong, Jung‐Wuk

doi:10.1016/j.autcon.2019.103019

Cited by 18 publications

(6 citation statements)

References 25 publications

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“…Crack recognition results based on classification networks and sliding windows can be further used for crack segmentation. The crack segmentation method used in this paper has poor robustness, and more accurate semantic segmentation methods and parameter extraction can be found in these papers (Jin et al, 2020;Ni et al, 2019;W. Wang et al, 2018;Tayo et al, 2019).…”

Section: Crack Pixel-levelmentioning

confidence: 99%

Crack detection using fusion features‐based broad learning system and image processing

Zhang

Yuen

2021

Computer aided Civil Eng

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Deep learning has been widely applied to vision-based structural damage detection, but its computational demand is high. To avoid this computational burden, a novel crack detection system, namely, fusion features-based broad learning system (FF-BLS), is proposed for efficient training without GPU acceleration. In FF-BLS, a convolution module with fixed weights is used to extract the fusion features of images. Feature nodes and enhancement nodes randomly generated by fusion features are used to estimate the output of the network. Meanwhile, the proposed FF-BLS is a dynamical system, which achieves incremental learning by adding nodes. Thus, the trained FF-BLS model can be updated efficiently with additional data, and this substantially reduces the training cost. Finally, FF-BLS was applied to crack detection. Compared with some well-known deep convolutional neural networks (VGG16, ResNet50, InceptionV3, Xception, and Efficient-Net), the FF-BLS achieved a similar level of recognition accuracy, but the training speed was increased by more than 20 times.

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Section: Crack Pixel-levelmentioning

confidence: 99%

Crack detection using fusion features‐based broad learning system and image processing

Zhang

Yuen

2021

Computer aided Civil Eng

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“…Another study made use of multiple visual sensors (e.g., digital camera, laser scanner and distance sensor) for concrete crack detection and measurement with the help of the YOLO (You Only Look Once)-v3-tiny model [164]. An autonomous crack width-measurement system using medial axis transform and flexible kernel was also proposed in another recent study [220].…”

Section: Surface-level Analysis: Concrete Crack Detectionmentioning

confidence: 99%

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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“…They obtained this by integrating the geometric correction index of the detected area and dividing it by the length. Jin et al (2020) used the medial axis transform (MAT) algorithm and the pruning algorithm to extract the crack skeleton line, and then used the algorithm based on the flexible kernel to calculate the width of the crack. Wang et al (2018) introduced the Laplace equation and used a crack blob extraction algorithm and a crack boundary extraction algorithm to measure the millimeter-level crack width of the crack image under ideal conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Jin et al. (2020) used the medial axis transform (MAT) algorithm and the pruning algorithm to extract the crack skeleton line, and then used the algorithm based on the flexible kernel to calculate the width of the crack. Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Real‐time automatic crack detection method based on drone

Meng

Gao

Zhou

et al. 2022

Computer aided Civil Eng

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Real-time automated drone-based crack detection can be used for efficient building damage assessment. This paper proposes an automated real-time crack detection method based on a drone. Using a lightweight classification algorithm, a lightweight segmentation algorithm, a high-precision segmentation algorithm, and a crack width measurement algorithm, the cracks are classified, roughly segmented, finely segmented, and the maximum width is extracted. A crack information-assisted drone flight automatic control algorithm for automatic crack detection guides the drone toward the crack position. The effectiveness of the crack detection algorithm and the crack information-assisted drone flight automatic control algorithm was tested on two different datasets, a two-story building, and a 16-m-high shaking table test building. The results show that crack detection can be finished in real-time during the flight. Using the proposed method can significantly improve the MIoU of crack edge detection and the accuracy of maximum crack width measurement under the non-ideal shooting conditions of the actual inspection situation by automatically approaching the vicinity of the crack.

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A vision-based approach for autonomous crack width measurement with flexible kernel

Cited by 18 publications

References 25 publications

Crack detection using fusion features‐based broad learning system and image processing

Crack detection using fusion features‐based broad learning system and image processing

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Real‐time automatic crack detection method based on drone

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