Development of Automated Algorithms for Pavement Condition Survey

Abstract

Image processing and analysis techniques have been successfully applied to the evaluation of some types of pavement surface distress, including longitudinal, transverse, and alligator cracking. Many efforts have been made to develop automated evaluation for other types of distress patterns, but, due to the complexities of these distress types, the results were not as positive as for those distresses mentioned above. The main objectives are to develop image segmentation and classification methods to isolate distress features, and to develop the back-propagation neural network model to recognize block cracking and potholes, in addition to alligator, longitudinal, and transverse cracking. It was observed that longitudinal, transverse, alligator, and block cracking were accurately recognized, and the analytical system has a success rate of 93 percent for potholes. Preliminary results indicate that the proposed approach is very positive and has great potential for integration into an automated system for pavement distress evaluation.

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