Image Processing–Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony

Hoang, Nhat‐Duc; Nguyen, Quoc-Lam; Bui, Dieu Tien

doi:10.1061/(asce)cp.1943-5487.0000781

Cited by 97 publications

(38 citation statements)

References 42 publications

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“…UAV is a flexible platform that can be configured with various remote sensing sensors, in which digital images are the most commonly used data type. Several methods [25,26,[32][33][34][35] that are based on digital image processing and machine learning algorithms have been proposed for pavement distress detection, using high-resolution images that were acquired from UAV platforms. For example, Kim utilized a simple UAV system to capture the pavement images and detected cracks based on the image binarization method [32].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

Cheng

Kwan

et al. 2019

IJGI

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Asphalt pavement ages and incurs various distresses due to natural and human factors. Thus, it is crucial to rapidly and accurately extract different types of pavement distress to effectively monitor road health status. In this study, we explored the feasibility of pavement distress identification using low-altitude unmanned aerial vehicle light detection and ranging (UAV LiDAR) and random forest classification (RFC) for a section of an asphalt road that is located in the suburb of Shihezi City in Xinjiang Province of China. After a spectral and spatial feature analysis of pavement distress, a total of 48 multidimensional and multiscale features were extracted based on the strength of the point cloud elevations and reflection intensities. Subsequently, we extracted the pavement distresses from the multifeature dataset by utilizing the RFC method. The overall accuracy of the distress identification was 92.3%, and the kappa coefficient was 0.902. When compared with the maximum likelihood classification (MLC) and support vector machine (SVM), the RFC had a higher accuracy, which confirms its robustness and applicability to multisample and high-dimensional data classification. Furthermore, the method achieved an overall accuracy of 95.86% with a validation dataset. This result indicates the validity and stability of our method, which highway maintenance agencies can use to evaluate road health conditions and implement maintenance.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

Cheng

Kwan

et al. 2019

IJGI

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“…Otsu method of image thresholding was employed to effectively binarize the image into crack and noncrack pixels [12]. Hoang et al [13] utilized the technique suggested by Talab et al [12] for crack detection in flexible pavements. Otsu method of thresholding was used to convert the gray scale image to a monochrome image [13].…”

Section: Image Processingmentioning

confidence: 99%

“…Hoang et al [13] utilized the technique suggested by Talab et al [12] for crack detection in flexible pavements. Otsu method of thresholding was used to convert the gray scale image to a monochrome image [13]. This method has the ability to distinct the crack pixels from the surrounding pixels and to separate the original grayscale image into foreground of crack pixels and background of pavement surface.…”

Section: Image Processingmentioning

confidence: 99%

“…The Otsu method (1979) [14] of image thresholding was applied to convert the gray level image to a binary image. This method assumes the images to contain two classes of pixels and iterates through all possible threshold values to compute a measure of spread for the pixels on each side of the threshold such that the original gray scale image is segmented into foreground of crack pixels and background of asphalt pavement [13]. The Otsu method may result in significant error if the sharp valley of the image histogram is degraded by excessive pavement noises.…”

Section: Image Processingmentioning

confidence: 99%

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Guidelines for Identification of Top-down Cracks (TDC) in In-Service Flexible Pavements

2019

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The objective of this study was to establish guidelines to identify top-down cracking (TDC) in flexible pavements using digital image analysis and the characteristics of these cracks. Past studies indicated that the time after construction of the pavement and location of the cracks are key parameters to identify top-down cracking. The cracks were reported to appear on the wheel path or at the outer edge of the wheel path typically within 3 to 8 years of construction. In-service pavement sections were selected for analysis based on the parameters identified from the literature and computer-vision techniques were employed to investigate the geometric characteristics of these cracks. Based on the results of the analysis, the average crack width was observed to be 3 to 7 mm. With respect to the orientation of the crack, the cracks segments were mostly longitudinal with typical deviation of 20 degrees. The orientation and intensity characteristics of top-down cracks were found as useful features in crack identification.

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“…Nevertheless, few studies have investigated the efficiency of the aforementioned image texture computation in spall recognition. Moreover, it is evident that the combination of image processing and machine learning can potentially lead to effective solutions for structure health monitoring [28][29][30][31][32][33][34]. However, models that hybridize the strengths of image processing and machine learning based classifiers have rarely been employed for spall detection.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis

2019

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Recognition of spalling on surface of concrete wall is crucial in building condition survey. Early detection of this form of defect can help to develop cost-effective rehabilitation methods for maintenance agencies. This study develops a method for automatic detection of spalled areas. The proposed approach includes image texture computation for image feature extraction and a piecewise linear stochastic gradient descent logistic regression (PL-SGDLR) used for pattern recognition. Image texture obtained from statistical properties of color channels, gray-level cooccurrence matrix, and gray-level run lengths is used as features to characterize surface condition of concrete wall. Based on these extracted features, PL-SGDLR is employed to categorize image samples into two classes of “nonspall” (negative class) and “spall” (positive class). Notably, PL-SGDLR is an extension of the standard logistic regression within which a linear decision surface is replaced by a piecewise linear one. This improvement can enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem. Experiments with 1240 collected image samples show that PL-SGDLR can help to deliver a good detection accuracy (classification accuracy rate = 90.24%). To ease the model implementation, the PL-SGDLR program has been developed and compiled in MATLAB and Visual C# .NET. Thus, the proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings.

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Image Processing–Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony

Cited by 97 publications

References 42 publications

RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

Guidelines for Identification of Top-down Cracks (TDC) in In-Service Flexible Pavements

Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis

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