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2. O. H. Kwon, D. N. Kim, and W. H. Jung, "A Study on Mission Flight Using Pixhawk-based Drones", Proc. of IEMEK Conference, Jeju, S. Korea, Vol. 1, pp. 349-351, 2019(in Korean). 

3. S. Murao, Y. Nomura, H, Furuta, and C. W. Kim, "Concrete Crack Detection Using UAV and Deep Learning", Proc. of ICASP13, Seoul, S. Korea, pp. 26-30, May 2019. 

4. C. X. Xu, J. P. Lim, M. Y. Park, and H. C. Yun, "A Proposal of Bridge Maintenance System Using Drone Image", Proc. of KSCE 2017 CONVENTION, Busan, S. Korea, pp. 694-695, 2017(in Korean). 

5. Kim, H., Ahn, E., Cho, S., Shin, M., Sim, S.H.. Comparative analysis of image binarization methods for crack identification in concrete structures. Cement and concrete research, vol.99, 53-61.

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6. Krizhevsky, Alex, Sutskever, Ilya, Hinton, Geoffrey E.. ImageNet classification with deep convolutional neural networks. Communications of the ACM, vol.60, no.6, 84-90.

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7. 10.1109/CVPR.2015.7298965 

   

8. Weng, Xingxing, Huang, Yuchun, Wang, Wenzong. Segment-based pavement crack quantification. Automation in construction, vol.105, 102819-.

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9. 10.1007/978-3-319-24574-4_28 

   

10. Choi, Keong-Hun, Ha, Jong-Eun. Edge Detection based-on U-Net using Edge Classification CNN. 제어·로봇·시스템학회 논문지 = Journal of institute of control, robotics and systems, vol.25, no.8, 684-689.

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11. Calar Fırat Ozgenel, "Concrete Crack Segmentation Dataset", Mendeley Data, Vol. 1, Mar. 2019. 

12. 10.1109/CVPR.2019.00075 

    

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