A Survey on Crack Detection Algorithms for Concrete Structures

Anitha, M. J.; Hemalatha, R.; Radha, S.

doi:10.1007/978-981-15-5029-4_53

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…A survey of crack detection algorithms including image processing, neural network, machine and deep learning based methods is available in the literature [55]. An 8-Mega Pixel mobile phone camera has captured crack initiation and propagation in green CBC samples during the compression test.…”

Section: Crack Detection Using Modified Spectral Clusteringmentioning

confidence: 99%

Crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

Vedrtnam

Kumar

Barluenga

et al. 2021

Preprint

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The present work aimed to develop an efficient way of capturing real-time crack propagation in concrete structures. The image processing was utilized for crack detection, while finite element modeling (FEM) and scanning electron microscopy (SEM) were used for quantitative and qualitative analysis of crack propagation. A green cement-based composite (CBC) containing saw dust was compared to a reference M20 grade concrete under compressive loading. Crack propagation during compression tests was captured using an 8-megapixel mobile phone camera. The randomly selected images showing crack initiation and propagation in CBCs were used to assess the crack capturing capability of a spectral analysis based algorithm. A measure of oriented energy was provided at crack edges to develop a similarity spatial relationship among the pairwise pixels. FE modelling was used for distress anticipation, by analyzing stresses during the compressive test in constituents of CBCs. SEM analyses were also done to evaluate cracked samples. It was found that FE modeling could predict the crack prone regions that can be used jointly with the image analysis algorithm, providing real-time inputs from the crack-prone areas. Green CBC were compared to reference concrete samples, showing reliable results. The replacement of OPC with wood dust reduced compression strength and produced a different fracture pattern regarding reference concrete. The results of the study can be used for distress anticipation and early crack detection of concrete structures for preventive support and management.

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Section: Crack Detection Using Modified Spectral Clusteringmentioning

confidence: 99%

Crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

Vedrtnam

Kumar

Barluenga

et al. 2021

Preprint

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“…For the last three decades, image-based crack detection (Mohan and Poobal, 2018;Koch et al, 2015) that include various image processing approaches, such as edge detection (Zhao et al, 2010;Hanzaei et al, 2017;Li et al, 2022), dynamic thresholding (Oliveira and Correia, 2009;Wang et al, 2021b), and different morphological operations (Anitha et al, 2021;Li and Zhao, 2021) have been the central focus for detecting damage in challenging real-world scenarios. However, the aforementioned methods are quite sensitive to noise and varying illumination intensities, and hence, not suitable in real-world complex conditions (Koch et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head

Roy¹,

Bhaduri²

2023

Preprint

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Objective. Computer vision-based up-to-date accurate damage classification and localization are of decisive importance for infrastructure monitoring, safety, and the serviceability of civil infrastructure. Current state-of-the-art deep learning (DL)-based damage detection models, however, often lack superior feature extraction capability in complex and noisy environments, limiting the development of accurate and reliable object distinction. Method. To this end, we present DenseSPH-YOLOv5, a real-time DL-based high-performance damage detection model where DenseNet blocks have been integrated with the backbone to improve in preserving and reusing critical feature information. Additionally, convolutional block attention modules (CBAM) have been implemented to improve attention performance mechanisms for strong and discriminating deep spatial feature extraction that results in superior detection under various challenging environments. Moreover, an additional feature fusion layers and a Swin-Transformer Prediction Head (SPH) have been added leveraging advanced self-attention mechanism for more efficient detection of multiscale object sizes and simultaneously reducing the computational complexity. Results. Evaluating the model performance in large-scale Road Damage Dataset

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“…Cracks on the surfaces of civil structures are important indicators of defect propagation and structural health. Most defects, including eforescence, water leakage, exfoliation, and separation, originate from cracks, suggesting that cracks can be considered as representative metrics of structural health [1,2]. It is important to detect these symptoms in advance because on-time operation and maintenance (O&M) not only decreases the repair cost and time signifcantly but also prevents the propagation of cracks and degradation of structures.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Structural Crack Detection through a Multiscale Multilevel Mask Deep Convolutional Neural Network and Line Similarity Index

Ham,

Jeong,

Kim

et al. 2023

International Journal of Intelligent Systems

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This paper proposes a novel and practical crack-detection method for infrastructure. The proposed method exhibits three key components. First, a multiscale multilevel mask deep convolutional neural network (MSML Mask DCNN) is proposed to accurately estimate crack candidates comprising linear and curvilinear features. Second, the proposed neural network is trained using only public image-sets. The main principle of this approach is that cracks have unique and distinct features, and therefore, public image-sets provide sufficient information to estimate crack candidates for a neural network. Third, a line similarity index (LSI), which is calculated using the Hough transform and coordinate transformation with principal component analysis, is incorporated to eliminate non-crack candidates from crack candidates based on two key characteristics: the variation in crack features with respect to the representative line and the number of crack features that crossed the representative line. Addressing these two crack-related characteristics improves accuracy and robustness by effectively eliminating non-crack features. Field tests performed inside a building and in an underground power tunnel demonstrated the effectiveness of the proposed method. The MSML Mask DCNN outperformed other neural networks, accurately recognizing local crack candidates characterized by linear and curvilinear features even though only public image-sets were used for training. The proposed LSI also effectively eliminated non-crack candidates estimated by the MSML Mask DCNN. The proposed method is practical for real-world applications, where several non-crack objects and noises are typically present.

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A Survey on Crack Detection Algorithms for Concrete Structures

Cited by 5 publications

References 23 publications

Crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

Crack detection using modified spectral clustering method assisted with FE analysis for distress anticipation in cement-based composites

A Computer Vision Enabled damage detection model with improved YOLOv5 based on Transformer Prediction Head

Enhancing Structural Crack Detection through a Multiscale Multilevel Mask Deep Convolutional Neural Network and Line Similarity Index

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