A review on pavement distress and structural defects detection and quantification technologies using imaging approaches

Chu, Chu; Wang, Linbing; Xiong, Hanguo

doi:10.1016/j.jtte.2021.04.007

Cited by 20 publications

(9 citation statements)

References 84 publications

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“…As a result, when the pavement surface being measured has the area of

, the resolution of the system can reach

. The system has several advantages, such as fast and efficient scanning mode [ 1 , 19 ]. Most of all, it exploits quasi-static measurement, which makes it more convenient to acquire high accuracy.…”

Section: The System Of 3d-ptrifmentioning

confidence: 99%

“…Thus, the 3D pavement texture measurement with high accuracy has been investigated. The 3D-PTRIF, which belongs to interferometry, has excellent accuracy and resolution in acquiring the texture depth data of the object surface [1]. In 1983, Takeda and Mutoh verified that profilometry based on interference fringe is capable of measuring 3D object shapes [2].…”

Section: Introductionmentioning

confidence: 99%

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Improved 3D Pavement Texture Reconstruction Method Based on Interference Fringe via Optimizing the Post-Processing Method

Chu

Wei

Wang

2023

Sensors

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The surface quality of pavement has a significant influence on the driving comfort and the skid resistance performance of roads. The 3D pavement texture measurement provides the basis for engineers to calculate the pavement performance index, such as the international roughness index (IRI), the texture depth (TD), and the rutting depth index (RDI), of different types of pavements. The interference-fringe-based texture measurement is widely used because of its high accuracy and high resolution, by which the 3D texture measurement has excellent accuracy in measuring the texture of workpieces with a diameter of <30 mm. When measuring the engineering products with a larger area (or larger areas), such as pavement surfaces, however, the accuracy is deficient because unequal incident angles due to the beam-divergence angle of the laser beam are ignored during the postprocessing of the measured data. This study aims to improve the accuracy of 3D pavement texture reconstruction based on the interference fringe (3D-PTRIF) by considering the influence of the unequal incident angles during postprocessing. It is found that the improved 3D-PTRIF has better accuracy than the traditional 3D-PTRIF, reducing the reconstruction errors between the measured value and the standard value by 74.51%. In addition, it solves the problem of a reconstructed slant surface, which deviates from the horizontal plane of the original surface. Compared to the traditional post-processing method, for the case of smooth surface, the slope can be decreased by 69.00%; for the case of coarse surface, the slope can be decreased by 15.29%. The results of this study will facilitate accurate quantifying of the pavement performance index by using the interference fringe technique, such as IRI, TD, and RDI.

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“…As a result, when the pavement surface being measured has the area of

, the resolution of the system can reach

Section: The System Of 3d-ptrifmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved 3D Pavement Texture Reconstruction Method Based on Interference Fringe via Optimizing the Post-Processing Method

Chu

Wei

Wang

2023

Sensors

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“…It is hypothesized that the non crack image's crack area can be obtained by averaging the pixels' grey levels along the linear object's inner and outer boundaries. First, the image is split into many overlapping sub image regions by [19]. Sub images were then segmented, and the cracks within them fused using the neighborhood difference histogram.…”

Section: Image Acquisitionmentioning

confidence: 99%

Detection of Road Cracks Using Convolutional Neural Networks and Threshold Segmentation

Ashraf

Sophian

Shafie

et al. 2022

JIAE

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Automatic road crack detection is an important transportation maintenance responsibility for ensuring driving comfort and safety. Manual inspection is considered to be a risky method because it is time consuming, costly, and dangerous for the inspectors. Automated road crack detecting techniques have been extensively researched and developed in order to overcome this issue. Despite the difficulties, most of the proposed methodologies and solutions involve machine vision and machine learning, which have lately acquired traction largely due to the increasingly more affordable processing power. Nonetheless, it remains a difficult task due to the inhomogeneity of crack intensity and the intricacy of the background. In this paper, a convolutional neural network-based method for crack detection is proposed. The method is inspired from recent advancements in applying machine learning to computer vision. The primary goal of this work is to employ convolutional neural networks to detect the road crack. Data in the form of images has been used as input, preprocessing and threshold segmentation is applied to the input data. The processed output is feed to CNN for feature extraction and classification. The training accuracy was found to be 96.20 %, the validation accuracy to be 96.50 %, and the testing accuracy to be 94.5 %.

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“…Flexible pavements are composed of different layers: surface, binder, base, and subbase courses. Distresses can be grouped into two different types (i.e., cracking and non-cracking), as shown in Figure 1 [3,4].…”

Section: Introductionmentioning

confidence: 99%

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

Guerrieri,

Parla,

Khanmohamadi

et al. 2024

Infrastructures

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Asphalt pavements are subject to regular inspection and maintenance activities over time. Many techniques have been suggested to evaluate pavement surface conditions, but most of these are either labour-intensive tasks or require costly instruments. This article describes a robust intelligent pavement distress inspection system that uses cost-effective equipment and the ‘you only look once’ detection algorithm (YOLOv3). A dataset for flexible pavement distress detection with around 13,135 images and 30,989 bounding boxes of damage was used during the neural network training, calibration, and validation phases. During the testing phase, the model achieved a mean average precision of up to 80%, depending on the type of pavement distress. The performance metrics (loss, precision, recall, and RMSE) that were applied to estimate the object detection accuracy demonstrate that the technique can distinguish between different types of asphalt pavement damage with remarkable accuracy and precision. Moreover, the confusion matrix obtained in the validation process shows a distress classification sensitivity of up to 98.7%. The suggested technique was successfully implemented in an inspection car. Measurements conducted on urban roads crossed by tramway lines in the city of Palermo proved the real-time ability and great efficacy of the detection system, with potentially remarkable advances in asphalt pavement examination efficacy due to the high rates of correct distress detection.

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A review on pavement distress and structural defects detection and quantification technologies using imaging approaches

Cited by 20 publications

References 84 publications

Improved 3D Pavement Texture Reconstruction Method Based on Interference Fringe via Optimizing the Post-Processing Method

Improved 3D Pavement Texture Reconstruction Method Based on Interference Fringe via Optimizing the Post-Processing Method

Detection of Road Cracks Using Convolutional Neural Networks and Threshold Segmentation

Asphalt Pavement Damage Detection through Deep Learning Technique and Cost-Effective Equipment: A Case Study in Urban Roads Crossed by Tramway Lines

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