Comparison of Supervised Classification Techniques for Vision-Based Pavement Crack Detection

In this study, the application of four classification techniques for computer vision-based pavement crack detection systems was investigated. The classification methods-artificial neural network (ANN), decision tree, k-nearest neighbor, and adaptive neuro-fuzzy inference system (ANFIS)-were selected on the basis of the complexity and clarity of their procedures. These methods were evaluated for (a) prediction performance, (b) computation time, (c) stability of results for highly imbalanced data sets, (d) stability of the classifiers' performance for pavements in different deterioration stages, and (e) interpretability of results and clarity of the procedure. According to the results, the ANN and ANFIS methods not only provide superior performance but also are more flexible and compatible for the crack detection application. The ANFIS method is called a white-box classifier, and the inferred knowledge from its membership functions can be used to characterize the imagery properties of detected image components.