Abstract
Asphalt pavement depression, e.g., cracking, rutting and bulges, are the main factors endangering transportation safety and capacity. Detection of these depression is a significant step for pavement management; to date several laser-scanning-based technologies have been implemented for this purpose. However, an automated solution remains a challenging task due to the complicated pavement conditions in real world such as illumination and shadows. In this paper, a vision-based automated detection method for pavement cracks is proposed using deep learning technology, wherein a convolutional neural network (CNN) is trained to learn the features of the cracks from images without any preprocessing. The designed CNN is trained on the image database containing 240 images, based on the open-source TensorFlow framework by Google Brain team, and consequently records with about 96% accuracy. The robustness and adaptability of the trained CNN are tested on 40 images taken from different roads under various crack types, which were not used in the training and validation process. Testing results show that the proposed method has satisfactory performance, and therefore, could be beneficial for providing an alternative solution to automated detection of pavement cracks.
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Data Availability
The database used to train the CNN of this study are available from the corresponding author upon request.
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Acknowledgements
This study was financially supported by the National Key R&D Program of China (Grant No. 2018YFC1505401); the National Natural Science Foundation of China (Grant No. 52078493); the Natural Science Foundation of Hunan (Grant No. 2018JJ3644); the Innovation Driven Program of Central South University (Grant No. 2019CX011). These financial supports are gratefully acknowledged. We would like to thank the developer team of the DualSPHysics, who developed the open-source SPH code and released it to the public. We also extend our gratitude to editor-in-chief and two reviewers for their insightful comments.
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Prof. Z. Han designed the study and wrote the paper. Prof. Y.F. Du collected images and built the database. H.X. Chen and Y.Q. Liu trained and tested the model. Prof. Y.G. Li and Prof. H. Zhang optimized the model. All authors discussed the results and commented on the manuscript.
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Submitted to Iranian Journal of Science and Technology, Transactions of Civil Engineering
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Han, Z., Chen, H., Liu, Y. et al. Vision-Based Crack Detection of Asphalt Pavement Using Deep Convolutional Neural Network. Iran J Sci Technol Trans Civ Eng 45, 2047–2055 (2021). https://doi.org/10.1007/s40996-021-00668-x
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DOI: https://doi.org/10.1007/s40996-021-00668-x