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A deep learning approach for pavement evaluation using 2D and 3D imaging systems
University of Central Florida.
University of Central Florida.
2018 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

Pavement condition evaluation is a key component of roadway performance assessment and management. The current pavement evaluation practice is mostly manual which is inefficient, inaccurate, subjective and sometimes dangerous. Due to the well-established limitations of manual inspection, computer vision-based systems have been considered as promising substitutes. The computer vision-based pavement evaluation systems, that have been proposed over the past decades use 2-dimensional, gray-scale pavement images and apply different sequences of various image processing and machine learning techniques to distinguish pavement defects from the image background. For this purpose, computer vision-based pavement evaluation systems must overcome several technical and practical challenges. Pavement background in 2D images are highly random, not only because asphalt has a random color texture but also due to random presence of water marks, oil stains, debris, pavement markings, and so forth. Most pavement defects (e.g. linear and fatigue cracks, patches, and potholes) also appear in highly random patterns. To handle this randomness, automated pavement evaluation systems usually employ a carefully designed system for each pavement defect or object. The general structure of such systems consists of 4 main components, including: 1) images processing methods to remove the random background of pavements and detect defect-like objects, 2) feature extraction, to compute the imagery descriptors that can distinguish actual defects from the remaining noise after the image processing, 3) supervised-machine learning techniques that use the extracted features to distinguish defects from the noise, and 4) pavement rating which uses the imagery characteristics of objects and defects to rate the pavement in accordance with different pavement rating manuals (e.g. ASTM, AASHTO and etc.).

Place, publisher, year, edition, pages
Linköping: Statens väg- och transportforskningsinstitut, 2018.
Research subject
X RSXC
Identifiers
URN: urn:nbn:se:vti:diva-12954OAI: oai:DiVA.org:vti-12954DiVA, id: diva2:1204623
Conference
18th International Conference Road Safety on Five Continents (RS5C 2018), Jeju Island, South Korea, May 16-18, 2018
Available from: 2018-05-16 Created: 2018-05-08 Last updated: 2018-05-25Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf