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A novel method for asphalt pavement crack classification based on image processing and machine learning

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Abstract

This study constructs an automatic model for detecting and classifying asphalt pavement crack. Image processing techniques including steerable filters, projective integral of image, and an enhanced method for image thresholding are employed for feature extraction. Different scenarios of feature selection have been attempted to create data sets from digital images. These data sets are then employed to train and verify the performance of machine learning algorithms including the support vector machine (SVM), the artificial neural network (ANN), and the random forest (RF). The feature set that consists of the properties derived from the projective integral and the properties of crack objects can deliver the most desirable outcome. Experimental results supported by the Wilcoxon signed-rank test show that SVM has achieved the highest classification accuracy rate (87.50%), followed by ANN (84.25%), and RF (70%). Accordingly, the proposed automatic approach can be helpful to assist transportation agencies and inspectors in the task of pavement condition assessment.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-03 Quang Trung, Da Nang, 550000, Vietnam

    Nhat-Duc Hoang

  2. Faculty of Civil Engineering, Duy Tan University, P202-03 Quang Trung, Da Nang, 550000, Vietnam

    Quoc-Lam Nguyen

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  1. Nhat-Duc Hoang
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  2. Quoc-Lam Nguyen
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Correspondence to Nhat-Duc Hoang.

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Hoang, ND., Nguyen, QL. A novel method for asphalt pavement crack classification based on image processing and machine learning. Engineering with Computers 35, 487–498 (2019). https://doi.org/10.1007/s00366-018-0611-9

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