Advanced Road Scene Image Segmentation and Pavement Evaluation Using Neural Networks. Final Report.

摘要

The current project, funded by MIOH-UTC for the period 9/1/2009-8/31/2010, continues our efforts in designing an image processing based pavement inspection system for the assessment of highway surface conditions. One of the most important tasks in pavement maintenance is pavement surface condition evaluation distress measurement. In order to eliminate the tedious and unreliable manual inspection of pavement surface evaluation, image processing and pattern recognition techniques are used to increase the efficiency and accuracy and decrease the costs of pavement distress measurements. Existing systems for automated pavement defect detection commonly require special devices such as lights, lasers, etc, which dramatically increase the cost and limit the system to certain applications. Therefore, in this report, 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.