건축 분야에서 활용되는 인공지능 기반의 이미지 프로세싱 방법 소개

  • 왕승현 (한양대학교 건축공학부)
  • Published : 2022.10.20

Abstract

Keywords

References

  1. J.K. Chow, K. fu Liu, P.S. Tan, Z. Su, J. Wu, Z. Li, Y.H. Wang, Automated defect inspection of concrete structures, Autom. Constr. (2021). https://doi.org/10.1016/j.autcon.2021.103959.
  2. C. Xiong, Q. Li, X. Lu, Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network, Autom. Constr. (2020). https://doi.org/10.1016/j.autcon.2019.102994.
  3. D. Gil, G. Lee, K. Jeon, Classification of images from construction sites using a deep-learning algorithm, in: ISARC 2018 - 35th Int. Symp. Autom. Robot. Constr. Int. AEC/FM Hackathon Futur. Build. Things, 2018. https://doi.org/10.22260/isarc2018/0024.
  4. Q. Fang, H. Li, X. Luo, L. Ding, H. Luo, T.M. Rose, W. An, Detecting non-hardhat-use by a deep learning method from far-field surveillance videos, Autom. Constr. (2018). https://doi.org/10.1016/j.autcon.2017.09.018.
  5. Y. Li, Y. Lu, J. Chen, A deep learning approach for real-time rebar counting on the construction site based on YOLOv3 detector, Autom. Constr. (2021). https://doi.org/10.1016/j.autcon.2021.103602.
  6. Y.J. Cha, W. Choi, G. Suh, S. Mahmoudkhani, O. Buyukozturk, Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types, Comput. Civ. Infrastruct. Eng. (2018). https://doi.org/10.1111/mice.12334.
  7. X. Zhou, Q. Gong, Y. Liu, L. Yin, Automatic segmentation of TBM muck images via a deep-learning approach to estimate the size and shape of rock chips, Autom. Constr. (2021). https://doi.org/10.1016/j.autcon.2021.103685.
  8. Z. Wang, Y. Zhang, K.M. Mosalam, Y. Gao, S.L. Huang, Deep semantic segmentation for visual understanding on construction sites, Comput. Civ. Infrastruct. Eng. (2022). https://doi.org/10.1111/mice.12701.