ASPHALT PAVEMENT CRACK DETECTION USING IMAGE PROCESSING AND NAÏVE BAYES BASED MACHINE LEARNING APPROACH

ナイーブベイズ法によるアスファルト舗装撮影画像からのひび割れ自動検出手法

For appropriate road maintenance and management of the road, detection and evaluation of the asphalt distress is very important. Standard test method for asphalt pavement in Japan have defined that crack ratio, amount of rutting, and flatness are main damage indices for the evaluation. Though the amount of rutting and the flatness can be obtained automatically from the pavement condition survey vehicles, the crack ratio is not automatically derived. Since this process requires to draw and count cracks manually from the photo of the road surface taken by the vehicle, enormous effort and time are required. In addition, the accuracy of the evaluation is doubt because the manual method does not evaluate the important information including the crack width. To solve these problems, this paper have developed automated asphalt pavement crack detection method using image processing technique and naïve Bayes based machine learning approach. The developed method is tested using the photos of various types of asphalt pavements, and it is found that the method can detect cracks with very high accuracy.　アスファルト舗装のひび割れ損傷を定量的に評価する指標としてひび割れ率が定められている．このひび割れ率の算出にあたっては路面のひび割れをスケッチした後に区画内のひび割れの本数を数える必要があるが，手作業となるため膨大な労力と時間が必要となり，さらにはひび割れ開口幅などの重要な情報を得ることができないという問題がある．そこで本研究ではナイーブベイズ法による機械学習と画像解析を組み合わせ，撮影画像からひび割れを自動的に検出する手法を構築した．本手法は画素単位でひび割れを検出できるため，上述のひび割れ開口幅や面積などについても計算が容易である．そして本手法を複数箇所の密粒度アスファルトおよびポーラスアスファルト舗装の路面から撮影された画像に適用した実験により，本手法の高いひび割れ検出性能を確認した．

• Record URL:
  • https://doi.org/10.2208/jscejpe.70.I_1
• Summary URL:
  • http://scholar.google.com/scholar_lookup?title=ASPHALT+PAVEMENT+CRACK+DETECTION+USING+IMAGE+PROCESSING+AND+NA%c3%8fVE+BAYES+BASED+MACHINE+LEARNING+APPROACH&author=P.+Chun&author=K.+HASHIMOTO&author=N.+KATAOKA&author=N.+KURAMOTO&author=M.+OHGA&publication_year=2015
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/21856559
• Authors:
  • Chun, Pang-jo
  • HASHIMOTO, Kazuaki
  • KATAOKA, Nozomu
  • KURAMOTO, Naoya
  • OHGA, Mitao
• Publication Date: 2015

Language

• English
• Japanese

Media Info

• Media Type: Web
• Pagination: pp I_1-I_8
• Serial:
  • Journal of Japan Society of Civil Engineers, Ser. E1 (Pavement Engineering)
  • Volume: 70
  • Issue Number: 3
  • Publisher: Japan Society of Civil Engineers
  • ISSN: 2185-6559
  • Serial URL: https://www.jstage.jst.go.jp/browse/jscejpe/-char/en

Subject/Index Terms

• TRT Terms: Asphalt pavements; Cracking; Highway maintenance; Image processing; Machine learning; Pavement distress
• Geographic Terms: Japan
• Subject Areas: Data and Information Technology; Highways; Materials; Pavements; I31: Bituminous Binders and Materials; I52: Construction of Pavements and Surfacings;

Filing Info

• Accession Number: 01564596
• Record Type: Publication
• Source Agency: Japan Science and Technology Agency (JST)
• Files: TRIS, JSTAGE
• Created Date: May 26 2015 4:12PM