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Classification of Road Pavement Defects Based on Convolution Neural Network in Keras

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Abstract

The aim of this paper is to propose the convolution neural network—VGG16 structure in Keras to classify pavement defects. In this paper, we present a method to build an automated system to classify different types of defects such as block cracks, longitudinal cracks and potholes. A region of interest is found and features are extracted using image processing techniques and machine learning methods. This system includes the following steps. The first step is to detect the defect location (ROI), then the defect is described by its features. Finally, each defect is classified based on these different features. The system ensures stable operation in the presence of limited lighting conditions, shadowing, and complex shaped defects.

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ACKNOWLEDGMENTS

The authors gratefully thank the referee for careful reading of the paper and valuable suggestions and comments.

Funding

This research was performed at the Baikal School of BRICS and the Laboratory of Artificial Intelligence and Machine Learning, Irkutsk National Research Technical University (Irkutsk, Russia) and was supported by University of Information and Communication Technology, Thai Nguyen University (no. DH2020-TN07-01). Authors also are thankful to the Center for Telecommunications and Multimedia, INESC TEC, Portugal for providing the dataset.

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Correspondence to H. T. Nguyen, L. T. Nguyen, A. D. Afanasiev or L. T. Pham.

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The authors declare that they have no conflicts of interest.

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Nguyen, H.T., Nguyen, L.T., Afanasiev, A.D. et al. Classification of Road Pavement Defects Based on Convolution Neural Network in Keras. Aut. Control Comp. Sci. 56, 17–25 (2022). https://doi.org/10.3103/S0146411622010084

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