Review and Analysis of Crack Detection and Classification Techniques based on Crack Types

Sitara, N. Sheerin; Kavitha, S.; Raghuraman, G.

doi:10.37622/ijaer/13.8.2018.6056-6062

Cited by 14 publications

(6 citation statements)

References 18 publications

(13 reference statements)

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“…The work proposed by Sheerin et al. (2018) implements Otsu's method for several works focused on the detection and classification of pavement cracks. They suggest a system that makes use of the wavelet transform and singular value decomposition (SVD) to extract and reduce the main features to perform the classification.…”

Section: Related Workmentioning

confidence: 99%

Efficient data dimensionality reduction method for improving road crack classification algorithms

Rodríguez-Lozano

Gámez-Granados

Palomares

et al. 2023

Computer aided Civil Eng

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Automatic crack classification plays an essential role in road maintenance. Using many features for the classification is inefficient for implementing embedded systems with low computational resources makes it difficult. Therefore, this work proposes a new data dimensionality reduction (DDR) for crack classification algorithms (DDR4CC). DDR4CC reduces the required information about the cracks to only four features. Using these features, the images can be classified into longitudinal, transverse, and alligator cracks or healthy pavement. DDR4CC is compared with eight DDR methods, and the reduced set of features is analyzed using five different classification algorithms. Besides, five different datasets, generated by a combination of several public datasets, are used. We are proposing a simple DDR method with high interpretability of the data, obtaining very fast computation and high accuracy. Experiments show that DDR4CC enhances the results of the classification algorithms, providing almost perfect classifiers with a minimum computation time.

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Section: Related Workmentioning

confidence: 99%

Efficient data dimensionality reduction method for improving road crack classification algorithms

Rodríguez-Lozano

Gámez-Granados

Palomares

et al. 2023

Computer aided Civil Eng

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“…Sheerin Sitara et al. (2021) used a support vector machine method to train statistical features and classify and identify cracks.…”

Section: Related Workmentioning

confidence: 99%

Image quality evaluation method for surface crack detection based on standard test chart

Zhu,

Huang,

Zhou

et al. 2023

Computer aided Civil Eng

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The use of automated equipment for surface crack detection based on digital image acquisition is becoming increasingly popular in the inspection industry. While researchers typically focus on improving the accuracy of recognition methods, the image quality is essential to the effectiveness of the algorithm. However, evaluating the quality of crack images has received little attention in computer‐aided civil and infrastructure engineering. A prominent issue is whether surface cracks are visible and measurable in images. This study proposes an image quality evaluation method using an original standard test chart that simulates cracks of different widths and directions. Geometric transformations and preprocessing techniques are employed in a full‐reference strategy to process the acquired crack images. The resulting information provides quantitative scores for crack visibility and measurability. The proposed Crack Structural Similarity Index is more in line with human perception and offers an accurate evaluation of real image quality. The study shows that Gaussian blur disturbance and random noise disturbance primarily affect measurability and visibility, respectively. Furthermore, the study finds that the quality of the crack image improves with increasing sensor pixel size and using a prime lens over a zoom or long zoom lens. This approach enables comparing image quality collected by different devices in the field environment and provides guidance for optimizing field acquisition parameters. In the future, the results of this study can be applied to facilitate the application of automated testing equipment and improve overall performance.

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“…Previous research from the literature indicates that the use of DSM-derived auxiliary layers has enhanced the accuracy assessment of land cover classification and that derived layers, such as slope data, have impacted the classification results when accompanied by range and intensity data (Shaker & El-Ashmawy, 2012). However, previous research that used LiDAR data for crack detection in pavement did not consider any of these derived products in their analyses (Scholar, 2018;Schnebele, Tanyu, Cervone, & Waters, 2015;Ragnoli, De Blasiis, & Di Benedetto, 2018).…”

Section: Acronyms and Definitionsmentioning

confidence: 99%

Potential Use of LiDAR Data for Crack Detection: A Case Study on Pavement Cracks

Alhomodi¹

2023

Preprint

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LiDAR (Light Detection and Ranging) is a technology that provides three-dimensional point cloud data with high spatial accuracy. It is increasingly used in various applications and disciplines, including engineering. Along with other capabilities, LiDAR systems are able to record reflected backscattered energy as intensity data and measured distances to targets as range data. As the laser signal wavelength that operates in LiDAR sensors is typically in the near-infrared (NIR) spectrum, high spectral reflectance separability can be observed and a number of different materials distinguished. These capabilities have motivated researchers to study the applicability of using LiDAR range and intensity data for pavement crack extraction. The main goal of the present research is to examine the potential use of LiDAR range and intensity data for automated pavement crack detection. The study explores the following: a) crack detection using the two image classification logics of pixel-based and object-based image classification; b) crack detection using the Maximum Likelihood Classifier (MLC), the Random Trees Classifier (RTC), and the Support Vector Machine (SVM) classifier; c) crack detection by conducting image classification of four scenarios of multi-layer images generated from LiDAR data; and d) crack detection using LiDAR data with different point spacing. The experimental results indicate that the use of LiDAR data independently is effective for pavement crack detection. In addition, the results show that object-based image classification logic optimizes classification accuracy and crack detection results, and that employing SVM enhances the classification process and improves crack detection accuracy. It is also observed that using range data separately produces high accuracy for the crack detection results while the accuracy is slightly reduced when intensity data are combined with range data compared with using range data only. Furthermore, in the classification process, adding slope and aspect layers to range and intensity data decreases the accuracy of crack detection results, while increasing LiDAR data point spacing reduces crack extraction accuracy.

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Review and Analysis of Crack Detection and Classification Techniques based on Crack Types

Cited by 14 publications

References 18 publications

Efficient data dimensionality reduction method for improving road crack classification algorithms

Efficient data dimensionality reduction method for improving road crack classification algorithms

Image quality evaluation method for surface crack detection based on standard test chart

Potential Use of LiDAR Data for Crack Detection: A Case Study on Pavement Cracks

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