Statistical Selection and Interpretation of Imagery Features for Computer Vision-Based Pavement Crack–Detection Systems

Mokhtari, Soroush; Wu, Liuliu; Yun, Hae‐Bum

doi:10.1061/(asce)cf.1943-5509.0001006

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…The authors also proposed a method to estimate the crack width for different pavement cracks to characterize the flexible pavements in terms of crack width; the width is the ratio of total area of the fragmented crack pixels to the total length of the connected crack line [10]. Mokhtari et al [11] used statistical approaches to analyze various features of cracks including area, length, width, orientation, intensity, texture roughness, and wheel path position using computer-vision techniques [11].…”

Section: Image Processingmentioning

confidence: 99%

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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Section: Image Processingmentioning

confidence: 99%

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

2019

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“…For instance, there are proposals focused on a binary classification to detect whether there is a crack or not (Mokhtari et al., 2017). Other proposals have been published centered on the classification of a single specific type of cracks, such as potholes (Jana et al., 2022), longitudinal and transverse cracks (Ibrahim et al., 2019), classification of lineal cracks (Liang et al., 2018), or even in the classification of raveling severity (Tsai et al., 2020).…”

Section: Introductionmentioning

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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“…To address these challenges, advanced IPT-based methods have been developed for extracting additional features of cracks beyond the edge feature. Mokhtari et al (2017) proposed a statistical approach based on multiple features of cracks including crack length, width, orientation, and so forth, enabling a more robust identification of pavement cracks. Jahanshahi and Masri (2012) applied a 3D reconstruction technology to create a 3D point cloud of a concrete crack to extract the crack penetration depth.…”

Section: Introductionmentioning

confidence: 99%

Vision‐Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking

Kong

2018

Computer aided Civil Eng

115

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Fatigue cracks developed under repetitive loads are one of the major threats to structural integrity of steel bridges. Human inspection is the most commonly applied approach for fatigue crack detection, but is time consuming, labor intensive, and lacks reliability. In this study, we propose a computer vision‐based fatigue crack detection approach using a short video stream taken by a consumer‐grade digital camera. A feature tracking technology is applied to the video for tracking the surface motion of the monitored structure under repetitive load. Then, a crack detection and localization algorithm is established to effectively search differential features at different video frames caused by the crack opening and closing. The effectiveness of the proposed approach is validated through testing two experimental specimens with in‐plane and out‐of‐plane fatigue cracks, respectively. Results indicate that the proposed approach can robustly identify the fatigue crack, even when the crack is under ambient lighting conditions, surrounded by other crack‐like edges, covered by complex surface textures, or invisible to human eyes due to crack closure. Furthermore, our proposed approach enables accurate quantification of the crack opening under fatigue loading with submillimeter accuracy. However, due to the capacity of the camera resolution in this study, accurate detection of crack tip remains challenging.

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Statistical Selection and Interpretation of Imagery Features for Computer Vision-Based Pavement Crack–Detection Systems

Cited by 11 publications

References 29 publications

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

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

Efficient data dimensionality reduction method for improving road crack classification algorithms

Vision‐Based Fatigue Crack Detection of Steel Structures Using Video Feature Tracking

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