Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

Abdellatif, Mohamed; Peel, Harriet; Cohn, Anthony G.; Fuentes, Raúl

doi:10.22260/isarc2019/0052

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…In addition to spaceborne and airborne hyperspectral imagers, UAV hyperspectral imaging systems that have gradually emerged in recent years have garnered increasing attention owing to their low cost and high spatial resolutions. These systems are mainly used for road condition detection and road material identification in specific areas [15,198]. However, their operating efficiency is lower than that of the spaceborne and airborne platforms, because of their low flight altitude.…”

Section: Main Methodsmentioning

confidence: 99%

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Review on Active and Passive Remote Sensing Techniques for Road Extraction

et al. 2021

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Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.

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Section: Main Methodsmentioning

confidence: 99%

“…Spaceborne hyperspectral imagers are primarily used to extract main roads, while airborne hyperspectral imagers can be used for road quality assessment and road condition monitoring. Computing the angle from spectral response UAV Detect pavement roads [198], 2019…”

Section: Main Methodsmentioning

confidence: 99%

Review on Active and Passive Remote Sensing Techniques for Road Extraction

et al. 2021

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“…This system uses the geographical information system data of the bridge (above, longitude coordinate system) to perform automatic measurements in conjunction with the mounted GPS while driving at high speed and thereby creates a database of the expansion gaps based on accurate survey images without affecting the traffic flow. In this study, a test survey was conducted on approximately 5000 bridges along the highway, and the analysis results were used for big-data-based machine learning for developing algorithms to accurately determine the length of the expansion joint gap depending on its type and site conditions (see Figure 3) [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62]. The NEXUS system and the on-site test survey introduction is available on our YouTube channel [63].…”

Section: Development Of Monitoring Technology For Bridge Expansion Joint Using Line-scan Camerasmentioning

confidence: 99%

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Kim

Cho

et al. 2021

ASI

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Recently, the lack of expansion joint gaps on highway bridges in Korea has been increasing. In particular, with the increase in the number of days during the summer heatwave, the narrowing of the expansion joint gap causes symptoms such as expansion joint damage and pavement blow-up, which threaten traffic safety and structural safety. Therefore, in this study, we developed a machine vision (M/V)-technique-based inspection system that can monitor the expansion joint gap through image analysis while driving at high speed (100 km/h), replacing the current manual method that uses an inspector to inspect the expansion joint gap. To fix the error factors of image analysis that happened during the trial application, a machine learning method was used to improve the accuracy of measuring the gap between the expansion joint device. As a result, the expansion gap identification accuracy was improved by 27.5%, from 67.5% to 95.0%, and the use of the system reduces the survey time by more than 95%, from an average of approximately 1 h/bridge (existing manual inspection method) to approximately 3 min/bridge. We assume, in the future, maintenance practitioners can contribute to preventive maintenance that prepares countermeasures before problems occur.

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“…In this paper, a new Asphalt Crack Index (ACI) is proposed to describe the spectra of road pavement and, in particular, assist the search for cracks. New roads are mainly composed of fresh asphalt (and newly formed cracks as well), while deteriorated roads show aged asphalt [11,18]. The difference in spectral response will be used to distinguish cracks from normal un-cracked road surface material.…”

Section: Introductionmentioning

confidence: 99%

Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

et al. 2020

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Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with background textures besides the lack of distinct features in cracks. Hyperspectral images are sensitive to surface material changes and their potential for road crack detection is explored here. The key observation is that road cracks reveal the interior material that is different from the worn surface material. A novel asphalt crack index is introduced here as an additional clue that is sensitive to the spectra in the range 450–550 nm. The crack index is computed and found to be strongly correlated with the appearance of fresh asphalt cracks. The new index is then used to differentiate cracks from road surfaces. Several experiments have been made, which confirmed that the proposed index is effective for crack detection. The recall-precision analysis showed an increase in the associated F1-score by an average of 21.37% compared to the VIS2 metric in the literature (a metric used to classify pavement condition from hyperspectral data).

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Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

Cited by 9 publications

References 22 publications

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Review on Active and Passive Remote Sensing Techniques for Road Extraction

Detection and Identification of Expansion Joint Gap of Road Bridges by Machine Learning Using Line-Scan Camera Images

Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index

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