Crack identification for rigid pavements using unmanned aerial vehicles

Ersoz, Ahmet Bahaddin; Pekcan, Onur; Teke, Turker

doi:10.1088/1757-899x/236/1/012101

Cited by 36 publications

(39 citation statements)

References 11 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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“…Technological innovations have helped researchers to develop ways to identify crack patterns in rigid pavements. [10] The rigid pavement data are taken from the data as built drawing of the Central Java Provincial Public Works Office in 2019. Below is presented the rigid pavement data used for analysis in the ATENA software, as presented in Table 1 The existing soil and rigid pavement data are then entered into the ATENA 3D software for further analysis of the deflection and crack patterns.…”

Section: Methodsmentioning

confidence: 99%

Crack Pattern Analysis of Plain Concrete Pavement due to Swelling Pressure on Expansive Soil

Afrianto¹,

Setyawan²,

Setiawan³

et al. 2022

cea

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“…In the second group, fully automated in the context of crack analysis, involves the use of digital recognition software capable of recognizing and quantifying grayscale variations that are related to cracks or cracks on a pavement surface [20]. Various authors [21][22][23][24][25][26] have published methods based on computational algorithms for automatic detection of deterioration; however, these have mainly focused on crack detection, with the exception of [27] which detects two types of deterioration.…”

Section: Traditional Methods For Inspecting Pavement Deteriorationmentioning

confidence: 99%

Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements

et al. 2020

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Pavement maintenance seeks to provide optimal service conditions. Before maintenance, it is necessary to know the condition of the pavement by inspection, a crucial step in deciding on the repair to be carried out. In this sense, unmanned aerial vehicles (UAVs) seem to be an economic substitute compared to the ground laser scanner for pavement inspection tasks. This research seeks to develop a method to measure potholes using 3D models generated with photographs acquired by a UAV and process them using a software based on the Structure from Motion-MultiView Stereo (SfM–MVS) technique. The contribution of this document is the proposal of recommendations for the acquisition of photographs for the realization of the models. To develop these recommendations, an experiment was carried out to evaluate the accuracy in the reconstruction of 3D models using images obtained from the variation and combination of flight planning parameters and data capture. Then, to validate these recommendations, a bumpy section of pavement was modeled using the SfM–MVS method. The results show that for heights of 10 and 15 m the use of this methodology is applicable for the measurement of the width and depth of potholes.

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Crack identification for rigid pavements using unmanned aerial vehicles

Cited by 36 publications

References 11 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

Crack Pattern Analysis of Plain Concrete Pavement due to Swelling Pressure on Expansive Soil

Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements

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