Unmanned aerial vehicle implementation for pavement condition survey

Astor, Yackob; Nabesima, Yasuyuki; Utami, Retno; Sihombing, Atmy Verani Rouly; Adli, Muhammad; Firdaus, Muhammad

doi:10.1016/j.treng.2023.100168

Cited by 12 publications

(7 citation statements)

References 41 publications

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“…The comparative accuracy of pavement distress dimensions in UAV-based models against manual field measurements reveals a significant correlation, with edge cracking and joint reflective cracks achieving the highest accuracy values. Moreover, the research by Astor et al (2023) [18] establishes a robust linear regression equation for the PCI method in UAV models, reinforcing the potential of UAV technology in automating and refining pavement condition surveys. This integration of UAV technology and image processing heralds a new era in pavement maintenance, where the rapid identification and classification of road distresses can be achieved with unprecedented precision and speed.…”

Section: Advancements In Image Processing For Pavement Analysismentioning

confidence: 84%

“…These models serve as a robust platform for the interpretation of pavement distresses, allowing for precise measurements of damage dimensions without the need for on-site manual surveys. Astor et al (2023) [18] have notably contributed to this field by integrating the Surface Distress Index (SDI) and Pavement Condition Index (PCI) methods with UAV-acquired models, comparing their efficacy against traditional field assessments. Their research underscores the higher precision and reliability of the PCI method when applied to UAV-derived pavement condition models.…”

Section: Advancements In Image Processing For Pavement Analysismentioning

confidence: 99%

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Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Makendran,

Karthik,

Hasan

et al. 2024

MATEC Web Conf.

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This paper presents the development of an innovative pavement maintenance and management system leveraging advanced drone imagery and Convolutional Neural Network (CNN) image classification. Our system is designed to perform 2D modelling of road surfaces using high-resolution images captured by drones. These images are then analysed by a CNN model specifically trained to detect and classify pavement damages in accordance with the IRC:82 'Code of Practice for Maintenance of Bituminous Surfaces of Highways'. The classification process identifies various types of road distresses such as cracks, potholes, and surface wear. Each identified distress is documented in a comprehensive report detailing the nature of the damage and recommending specific remedies as per IRC guidelines. Furthermore, the system categorizes the severity of the damages, facilitating the dispatch of these results to maintenance authorities for immediate action. This ensures that repair efforts are prioritized effectively, contributing to the maintenance of safer and higher quality roadways. By automating the detection and classification of road damages, this system not only accelerates the repair process but also plays a crucial role in reducing road accidents by maintaining better road conditions. This approach showcases the potential of integrating artificial intelligence and drone technology in the field of road maintenance, marking a significant step towards smarter and safer road infrastructure.

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Section: Advancements In Image Processing For Pavement Analysismentioning

confidence: 84%

Section: Advancements In Image Processing For Pavement Analysismentioning

confidence: 99%

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Makendran,

Karthik,

Hasan

et al. 2024

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…Pavement video or image data are collected by cameras or drones. With the advancement of image processing techniques, scholars can identify pavement cracks, ruts, and roughness from images captured by cameras [1,15,16] or aerial images recorded by Unmanned Aerial Vehicles (UAVs) [17][18][19] or Google Maps [20,21]. Comparing cameras mounted on vehicles, UAVs offer a broader perspective and can cover larger areas quickly, yet they cannot capture detailed characteristics of pavement surfaces.…”

Section: Pavement Data Sourcementioning

confidence: 99%

“…Meanwhile, a large portion of pothole reports are not matched with maintenance requests, yet they are likely to be other pavement distress or missing potholes [3]. Astor, Nabesima [17], Zhao, Zhou [18], Cardenal, Fernández [19], Han, Chung [20],…”

Section: Pavement Data Sourcementioning

confidence: 99%

Estimating Pavement Condition by Leveraging Crowdsourced Data

Gu,

Khojastehpour,

Jia

et al. 2024

Remote Sensing

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Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation.

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“…With the fast development of intelligent transportation systems, multiple sensors including LiDAR and cameras are available on the ground/aerial autonomous vehicles [4][5][6][7] that have the potential to be used to assess the pavement conditions [8]. In a recent study [9], the Unmanned Aerial Vehicle (UAV) was utilized for assessing pavement conditions using the Pavement Condition Index and Surface Distress Index. These methods gain popularity due to the speed of the data acquisition which is one of the major advantages.…”

Section: Introductionmentioning

confidence: 99%

Using NDT Data to Assess the Effect of Pavement Thickness Variability on Ride Quality

2023

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Pavement condition largely determines its long-term behavior and is of paramount importance for rehabilitation and maintenance management. The use of non-destructive testing (NDT) systems to assess pavement condition has gained much popularity. Often, well-known NDT systems are combined to take full advantage of the capabilities of each system. Combining independent NDT systems to optimize the assessment process is a scientific challenge. With this in mind, the purpose of this paper is to investigate the extent to which data from two independent NDT systems can be combined: pavement thickness obtained with ground penetrating radar (GPR) and roughness data obtained with a road surface profiler (RSP). In particular, the objective of this study is to determine whether the expected variations in asphalt layer thickness, due to the construction process and the different pavement cross sections along the same road/highway road, may have an impact on pavement roughness as expressed in International Roughness Index (IRI) values. GPR and roughness data are collected, processed, and analyzed. The analysis results show that thickness variations are reflected in pavement roughness. The greater the variation in asphalt layer thickness, the greater the IRI values. Furthermore, it is argued that the GPR capabilities can be used for an initial assessment of the expected pavement quality.

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Unmanned aerial vehicle implementation for pavement condition survey

Cited by 12 publications

References 41 publications

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Designing an Intelligent Pavement Maintenance and Management System using Drone Imagery and Artificial Intelligence

Estimating Pavement Condition by Leveraging Crowdsourced Data

Using NDT Data to Assess the Effect of Pavement Thickness Variability on Ride Quality

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