A framework for pavement crack detection and classification

Abstract

Pavement damage detection is indeed a very important process for the management of roads. Nowadays, scholars are focusing on finding a simple and accurate way to detect road cracks aiming to increase its life span and improve its safety and quality. However, due to some factors including cost of implementation and level of experiences required small communities and developing countries are yet unable to adapt this idea. Therefore, developing a feasible method is becoming extremely important for improving the quality and safety of pavements. Thus, this study is intended to propose a framework for pavement crack detection and classification through the use of inexpensive set of sensors and the applications of artificial neural networks. As part of the project, difficulties that are going to be faced by local agencies in small communities will be highlighted and possible solutions will be suggested. In general, this framework is expected to be helpful to government entities in developing plans and taking actions toward providing a suitable pavement distress mitigation strategy.