Transportation informatics : an image analysis system for managing transportation facilities – phase II.

Details

• Creators:
  Chou, Eddie Y. ; Salari, Ezzatollah
• Corporate Creators:
  University of Toledo. University Transportation Center
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Transportation, hydraulic and utility facilities
  • Highway facilities
  • Roads
  • Transportation, hydraulic and utility facilities
  • Highway facilities
  • Roads
• Resource Type:
  Tech Report
• Geographical Coverage:
  Ohio
• Corporate Publisher:
  University of Toledo. University Transportation Center
• Abstract:
  One of the most important tasks in maintaining transportation facilities such as highways ; and streets is the evaluation of the existing condition. Visual evaluation by human ; inspectors is subjective in nature, therefore has issues of consistency and the speed and ; frequency of evaluation is limited due to the manual process. Automated evaluation ; using modern digital image processing and pattern recognition techniques can increase ; the efficiency and accuracy and decrease the costs of condition evaluation. Several ; automated condition evaluation systems have been developed, but these systems ; commonly require special devices such as strobe light, laser beams, etc, which increase ; the cost and limit the system to certain applications. In this study, a low cost automatic ; pavement distress evaluation approach is presented. This method can provide real-time ; pavement distress detection as well as evaluation results based on color images captured ; from a camera installed on a survey vehicle. The entire process consists of two main ; parts: pavement surface extraction followed by pavement distress detection and ; classification. In the first part, a novel color segmentation method based on a feed ; forward neural network is employed to separate the road surface from the background. ; In the second part, a segmentation technique based on probabilistic relaxation is used to ; separate distress areas from the road surface. The geometrical parameters obtained from ; the detected distresses are then fed to a neural network based pavement distress classifier ; in which the defects are classified into different types. Simulation results are given to ; show that the scheme presented in this report is both effective and reliable on a variety of ; pavement images.
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• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24092/dot_24092_DS1.pdf
• File Type:
  [PDF - 1.83 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:536319fe67b1676f0810769c70fd56d214639c84585d822e4bafb2e22cae888f5ef230f9d683db733058271ab9d81e179aa3c092b2f4fa7ab66a43f731b96f74