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

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  • English

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    • 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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