An approach for the automated extraction of road surface distress from a UAV-derived point cloud

Biçici, Serkan; Zeybek, Mustafa

doi:10.1016/j.autcon.2020.103475

Cited by 57 publications

(43 citation statements)

References 48 publications

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“…GAPs, GaMM and EdmCrack600 are a datasets containing video imaging. 2) UAV datasets: Some studies [10,20,21] were used UAVs (Unmanned Aerial Vehicles) to inspect pavement condition. Aerospace imagery was applied in several domains (agricultural, mapping, forestry, surveillance, etc.)…”

Section: ) Vision Datasetsmentioning

confidence: 99%

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Automated Pavement Distress Detection, Classification and Measurement: A Review

Benmhahe¹,

Chentoufi²

2021

IJACSA

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Road surface distress is an unavoidable situation due to age, vehicles overloading, temperature changes, etc. In the beginning, pavement maintenance actions took only place after having too much pavement damage, which leads to costly corrective actions. Therefore, scheduled road surface inspections can extend service life while guaranteeing users security and comfort. Traditional manual and visual inspections don't meet the nowadays criteria, in addition to a relatively high time volume consumption. Smart City pavement management preventive approach requires accurate and scalable data to deduce significant indicators and plan efficient maintenance programs. However, the quality of data depends on sensors used and conditions during scanning. Many studies focused on different sensors, Machine Learning algorithms and Deep Neural Networks tried to find a sustainable solution. Besides all these studies, pavement distress measurement stills a challenge in Smarts Cities because distress detection is not enough to decide on maintenance actions required. Damages localization, dimensions and future development should be highly detected on real-time. This paper summarizes the state-of-the-art methods and technologies used in recent years in pavement distress detection, classification and measurement. The aim is to evaluate current methods and highlight their limitations, to lay out the blueprint for future researches. PMS (Pavement Management System) in Smarts Cities requires an automated pavement distress monitoring and maintenance with high accuracy for large road networks.

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Section: ) Vision Datasetsmentioning

confidence: 99%

“…3) UAV images extraction: M. Zeybek [10,20] used the mini UAV DJI Phantom 4 RTK (P4RTK) to inspect the condition of a road segment (100Meters) contains small cracks, potholes and ruts. The proposed method constructed on three phases: 712 | P a g e www.ijacsa.thesai.org…”

Section: Extraction and Analysismentioning

confidence: 99%

Automated Pavement Distress Detection, Classification and Measurement: A Review

Benmhahe¹,

Chentoufi²

2021

IJACSA

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“…UAVs can be integrated in various applications of the transport engineering sector, such as road safety inspections, traffic analysis and damage assessment for roads [21][22][23][24][25]. The benefits of deploying UAVs for traffic monitoring and analysis have been considered in several studies in the past few years [5,20,[26][27][28].…”

Section: Uav Technology For Traffic Surveys and Analysismentioning

confidence: 99%

Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

Anagnostopoulos

Kehagia

2021

Infrastructures

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Research into collecting and measuring reliable, accurate, and naturalistic microscopic traffic data is a fundamental aspect in road network planning scientific literature. The vehicle trajectory is one of the main variables in traffic flow theory that allows to extract information regarding microscopic traffic flow characteristics. Several methods and techniques have been applied regarding the acquisition of vehicle trajectory. The forthcoming applications of intelligent transport systems on vehicles and infrastructure require sufficient and innovative tools to calibrate existing models on more complex situations. Unmanned aerial vehicles (UAVs) are one of the most emerging technologies being used recently in the transportation field to monitor and analyze the traffic flow. The aim of this paper is to examine the use of UAVs as a tool for microscopic traffic data collection and analysis. A comprehensive guiding framework for accurate and cost-effective naturalistic traffic surveys and analysis using UAVs is proposed and presented in detail. Field experiments of acquiring vehicle trajectories on two multilane roundabouts were carried out following the proposed framework. Results of the experiment indicate the usefulness of the UAVs technology on various traffic analysis studies. The results of this study provide a practical guide regarding vehicle trajectory acquirement using UAVs.

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“…The mapping of road surface and its surroundings is important for various applications ranging from road safety and planning to road comfort (Biçici and Zeybek, 2021;Zeybek and Şanlıoğlu, 2019). Therefore, several methods and technologies have been introduced to obtain road surface and its environment.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, several methods and technologies have been introduced to obtain road surface and its environment. One common way is to extract road surfaces from three-dimensional (3D) data (Biçici and Zeybek, 2021;Jiménez-Jiménez et al 2021;Zeybek and Şanlıoğlu, 2019). There are several technologies to collect 3D data.…”

Section: Introductionmentioning

confidence: 99%

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

Zeybek¹,

Biçici²

2021

Turkish Journal of Remote Sensing and GIS

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Unmanned Aerial Vehicle (UAV) technology is one of the fastest-growing technologies especially used in image processing. Structure-from-Motion (SfM) based software are usually used to convert two-dimensional UAV-based images into three-dimensional (3D) data. Then, objects such as buildings, trees, and roads can be classified from the 3D data for further analysis. In this study, the road surface generated from 3D data was evaluated. There are several factors that affect the accuracy of the 3D data. In this study, two factors, namely UAV flight altitude and SfM based software, were evaluated. Two different flight altitudes, which were 35 meters and 50 meters, were used. It was found that the lower flights with closer altitudes did not make a significant difference on the results and produced similar results. Another factor is different SfM based software. Two well-known SfM based software were used in this study, which were the Agisoft Metashape and Pix4D Mapper. In this case study, it was found that the Agisoft Metashape software produced more accurate and faster results than Pix4D Mapper software.

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An approach for the automated extraction of road surface distress from a UAV-derived point cloud

Cited by 57 publications

References 48 publications

Automated Pavement Distress Detection, Classification and Measurement: A Review

Automated Pavement Distress Detection, Classification and Measurement: A Review

Utilizing UAVs Technology on Microscopic Traffic Naturalistic Data Acquirement

3D Dense Reconstruction of Road Surface from UAV Images and Comparison of SfM Based Software Performance

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