Automatic Pavement Cracks Detection using Image Processing Techniques and Neural Network

Feature extraction methods and subsequent neural network performances were used in this research to impose proper assessment for distressed roads for a case study area in the North of Jordan. Object recognition method was used to extract roads cracks from airborne images acquired by drones. After images has been thresholded and the noise removed, digital image processing algorithms were applied to detect the presence of different crack types in the surface of pavement. In addition to that, the process was capable to automatically determine the length and the orientation of the cracks which were used as input for a neural network pattern recognition function designed for this purpose. Artificial Neural Network was used, tested and verified for cracks extraction. Different patterns and numbers of hidden layers were also investigated. The results revealed that using image processing techniques and neural network could detect pavement cracks with high accuracy.