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:
  Highway Facilities Roads Transportation, Hydraulic And Utility Facilities
• 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.
  
  
• Format:
  PDF
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha256:cfbdbcb057d97ba0e3a1f6c81ee6b49b7d93c4e3e4f960636f88567cf1647378
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/24092/dot_24092_DS1.pdf
• File Type:
  [PDF - 1.83 MB ]