Transportation informatics : advanced image processing techniques automated pavement distress evaluation.

Details

• Creators:
  Salari, Ezzatollah ; Chou, Eddie Yein Juin ; Lynch, James ; Dutta, Utpal
• Corporate Creators:
  Michigan Ohio University Transportation Center
• Corporate Contributors:
  University of Toledo. Dept. of Electrical Engineering & Computer Science ; University of Detroit Mercy. Civil, Architectural & Environmental Engineering ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; Michigan State Transportation Commission ; Michigan. Dept. of Transportation ; United States. Federal Highway Administration
• Subject/TRT Terms:
  • Intelligent transportation systems
  • Image processing
  • Pavement maintenance
  • Pavement management systems
  • Inspection
  • Pavement distress Intelligent transportation systems
  • Image processing
  • Pavement maintenance
  • Pavement management systems
  • Inspection
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• Publication/ Report Number:
  MIOH UTC TS18p2 2010-Final
• Resource Type:
  Tech Report
• Geographical Coverage:
  Michigan;Ohio
• Edition:
  Final report.
• Corporate Publisher:
  Michigan Ohio University Transportation Center
• Abstract:
  The current project, funded by MIOH-UTC for the period 1/1/2009- 4/30/2010, is concerned ; with the development of the framework for a transportation facility inspection system using ; advanced image processing techniques. The focus of this study is on the technical details of ; investigating and utilizing state-of-the-art image analysis techniques to further advance research ; in image processing based inspection systems in order to detect and classify the cracks in ; pavement. The detection of cracks and other degradations of pavement surfaces has traditionally ; been done by human experts conducting visual inspection while driving along the surveyed road. ; This manual approach is not only time consuming but also costly and subjective. To overcome ; these limitations we developed two different approaches for automatic crack detection and ; classification to speed up the process and reduce subjectivity. In the first approach, after the ; pavement images are captured by a digital camera, regions corresponding to cracks are detected ; over the acquired images by local segmentation and then represented by a matrix of square tiles. ; Since the crack pattern can be represented by the distribution of the crack tiles, standard ; deviations of both vertical and horizontal histograms are calculated to map the cracks onto a 2D ; feature space, where four crack types can be identified as: longitudinal cracks, transversal cracks, ; block cracks and alligator cracks. This new technique provides a low-cost, near real time distress ; analysis option. In the second approach we explore the use of a more robust multi-resolution ; scheme based on the beamlet transform. This method uses a pavement distress image ; enhancement algorithm to correct the non-uniform background illumination by calculating the ; multiplicative factors that eliminate the background lighting variations. ; To extract the linear features such as surface cracks from the pavement images, the image is ; partitioned into small windows and a beamlet transform based algorithm is applied. The crack ; segments are then linked together and classified into four types, vertical, horizontal, transversal, ; and block types. Simulation results show that the method is effective and robust in the extraction ; of cracks from a variety of pavement images. The experimental results, obtained by testing real ; pavement images over local asphalt roads, present the effectiveness of our algorithm for ; automating the process of identifying road distresses from images.
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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24576/dot_24576_DS1.pdf
• File Type:
  [PDF - 1.70 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:1196e7f3232bf7f6dc6a35ec370d7f7954c6320137143f0022341ab8d6aead4e5decf63cbdaa760ef737b7dd7fcac21b08dc060c81064a0e854c24d45c2ee857