The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Road crack detection using deep convolutional neural network
Abstract
Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Improving Computer-Aided Detection UsingConvolutional Neural Networks and Random View Aggregation
Roth, H. R., Lu, L., Liu, J., Yao, J., Seff, A., Cherry, K., Kim, L., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2015.2482920 · Cited by 608 · cited 1×
“Inspired by the method proposed in [11], the probability of a point can be calculated by averaging probability {P1, .”
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Cited by
Autonomous concrete crack detection using deep fully convolutional neural network
Dung, C. V., & Anh, L. D. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.11.028 · Cited by 1,170
Dorafshan, S., Thomas, R. J., & Maguire, M. (2018) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2018.08.011 · Cited by 774
Azimi, M., Eslamlou, A., & Pekcan, G. (2020) · Sensors · doi:10.3390/s20102778 · Cited by 769
Surface Defect Detection Methods for Industrial Products: A Review
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457
Enhancing transportation systems via deep learning: A survey
Wang, Y., Zhang, D., Liu, Y., Dai, B., & Lee, L. H. (2019) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2018.12.004 · Cited by 312
Metadata
- Authors
- Zhang, L., Yang, F., Daniel Zhang, Y., & Zhu, Y. J.
- Venue
- 2016 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2016-09
- Pages
- 3708-3712
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2511065100
- Cited by
- 1,496
- References
- 5
- FWCI
- 255.94
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, L., Yang, F., Daniel Zhang, Y., & Zhu, Y. J. (2016). Road crack detection using deep convolutional neural network. 2016 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2016.7533052
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