Comparison of Deep Convolutional Neural Networks and Histogram of Oriented Gradients Based Feature Extraction in Concrete Bridge Crack Images
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-26852-6_56
Reference22 articles.
1. Omar, T., Nehdi, M.: Condition assessment of reinforced concrete bridges: current practice and research challenges. Infrastructures 3(3), 36 (2018). https://doi.org/10.3390/infrastructures3030036
2. Dorafshan, S., Thomas, R.J., Maguire, M.: “Benchmarking image processing algorithms for unmanned aerial system-assisted crack detection in concrete structures. Infrastructures 4(2), 19 (2019). https://doi.org/10.3390/infrastructures4020019
3. Abdel-Qader, I., Abudayyeh, O., Kelly, M.E.: Analysis of edge-detection techniques for crack identification in bridges. J. Comput. Civ. Eng. 17(4), 255–263 (2003). https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255)
4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893, June 2005. https://doi.org/10.1109/CVPR.2005.177
5. Meng, L., Wang, Z., Fujikawa, Y., Oyanagi, S.: Detecting cracks on a concrete surface using histogram of oriented gradients. In: 2015 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 103–107, August 2015. https://doi.org/10.1109/ICAMechS.2015.7287137
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