Pavement crack image acquisition methods and crack extraction algorithms: A review

The extraction of pavement cracks is always a hard task in image processing. In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously. Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc. Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images. From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images. From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years. There are many methods and algorithms that are satisfactory in pavement crack applications, but there is no standard until today. Therefore, in order to know the developing history and the advanced research, the authors have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms. Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made.

• Record URL:
  https://doi.org/10.1016/j.jtte.2019.10.001
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2095756419303010
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20957564
• Supplemental Notes:
  • © 2019 Periodical Offices of Chang'an University. Publishing services by Elsevier B.V. on behalf of Owner. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Weixing
  • Wang, Mengfei
  • Li, Hongxia
  • Zhao, Heng
  • Wang, Kevin
  • He, Changtao
  • Wang, Jun
  • Zheng, Sifan
  • Chen, Jiabin
• Publication Date: 2019-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 535-556
• Serial:
  • Journal of Traffic and Transportation Engineering (English Edition)
  • Volume: 6
  • Issue Number: 6
  • Publisher: Elsevier
  • ISSN: 2095-7564
  • Serial URL: http://www.journals.elsevier.com/journal-of-traffic-and-transportation-engineering-english-edition
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Algorithms; Highway engineering; Image processing; Pavement cracking; Pavements; Reviews
• Subject Areas: Highways; Pavements;

Filing Info

• Accession Number: 01729344
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 29 2020 2:30PM