Abstract
Periodic surveys of asphalt pavement condition are very crucial in road maintenance. This work carries out a comparative study on the performance of machine learning approaches used for automatic pavement crack recognition. Six machine learning approaches, Naïve Bayesian Classifier (NBC), Classification Tree (CT), Backpropagation Artificial Neural Network (BPANN), Radial Basis Function Neural Network (RBFNN), Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM), have been employed. Additionally, Median Filter (MF), Steerable Filter (SF), and Projective Integral (PI) have been used to extract useful features from pavement images. In the feature extraction phase, performance comparison shows that the input pattern including the diagonal PIs enhances the classification performance significantly by creating more informative features. A simple moving average method is also employed to reduce the size of the feature set with positive effects on the model classification performance. Experimental results point out that LSSVM has achieved the highest classification accuracy rate. Therefore, this machine learning algorithm used with the feature extraction process proposed in this study can be a very promising tool to assist transportation agencies in the task of pavement condition survey.
Highlights
The acceptable level of road serviceability is very crucial to ensure the economic growth and the safety of passengers
The current study extends the body of knowledge in the following aspects: (i) To deal with the complex and noisy texture of the pavement background, image processing techniques including Median Filter, Steerable Filter, and Projective Integral are used in the feature extraction phase
Besides the two models of Backpropagation Artificial Neural Network (BPANN) and Radial Basis Function Neural Network (RBFNN) which can be directly used for multiclass classification, the two-class classification versions of Naıve Bayesian Classifier (NBC), DT, Support Vector Machine (SVM), and Least Squares Support Vector Machine (LSSVM) are extended with one-versus-one (OvO) strategy [59] to deal with the multiclass nature of the pavement crack classification at hand
Summary
The acceptable level of road serviceability is very crucial to ensure the economic growth and the safety of passengers. Hoang and Nguyen [2] employed the image processing methods of Steerable Filters and Projective Integral for the feature extraction task as well as machine learning for classification task. More studies should be dedicated to improving the effectiveness of pavement classification models This improvement can be achieved either through the enhancement of the feature extraction phase or through the identification of more suitable machine learning approaches. Based on such motivations, this study proposes an alternative tool for automatic pavement crack classification that employs image processing and machine learning methods. The subsequent part of the article is organized as follows: The second section reviews the research methodology; the third section presents the processes of image acquisition and feature extraction followed by the experimental result and comparison; the last section summarizes the study with several remarks
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