과제정보
본 연구는 국토교통부 디지털 기반 건축시공 및 안전감리기술개발 사업(1615012983)과 2021년도 한국연구재단에서 지원하는 기초연구실(No. 2021R1A4A3030117)의 연구비 지원 의해 수행되었습니다.
참고문헌
- Park, H. S. (2021), Land and Transformation, Construction Transportation Journal, Seoul.
- Kim, J. C., Shin, S. H., and Oh, S. H. (2019), Damage Investigation of Pilotis Structures and Analysis of Damage Causes by Pohang Earthquake, Journal of the Architectural Institute of Korea Structure & Construction, AIK., 35(3), 3-10. https://doi.org/10.5659/JAIK_SC.2019.35.3.3
- ANCnews. (2018), Available at: http://www.ancnews.kr/news/articleView.html?idxno=6403
- Zhang, D., Xie, Z., and Wang, C. (2008), Bar section image enhancement and positioning method in on-line steel bar counting and automatic separating system, 2008 Congress on Image and Signal Processing, IEEE, 2, 319-323.
- Ying, X., Wei, X., Pei-xin, Y., Qing-da, H., and Chang-hai, C. (2010), Research on an Automatic Counting Method for Steel Bars' Image, 2010 International Conference on Electrical and Control Engineering, IEEE, 1644-1647.
- Fan, Z., Lu, J., Qiu, B., Jiang, T., An, K., Josephraj, A. N., and Wei, C. (2019), Automated steel bar counting and center localization with convolutional neural networks, arXiv preprint arXiv, 1906.00891.
- Yang, H., and Fu, C. (2019), Quantity Detection of Steel Bars Based on Deep Learning, Open Access Library Journal, OALib, 6(10), 1-9.
- Zhu, Y., Tang, C., Liu, H., and Huang, P. (2020), End-face localization and segmentation of steel bar based on convolution neural network, Journal of IEEE Access, IEEE, 8, 74679-74690. https://doi.org/10.1109/ACCESS.2020.2989300
- Li, Y., Lu, Y., and Chen, J. (2021), A deep learning approach for real-time rebar counting on the construction site based on YOLOv3 detector, Automation in Construction, Elsevier, 124, 103602. https://doi.org/10.1016/j.autcon.2021.103602
- Shin, Y., Heo, S., Han, S., Kim, J., and Na, S. (2021), An Image-Based Steel Rebar Size Estimation and Counting Method Using a Convolutional Neural Network Combined with Homography, Buildings, MDPI, 11(10), 463. https://doi.org/10.3390/buildings11100463
- Zhang, J., Mo, J., Xu, H., and Liu, Z. (2020), A semantic segmentation method for exposed rebar on dam concrete based on Unet, Journal of Physics, IOP, 1651, 012169
- Yuan, X., Smith, A., Sarlo, R., Lippitt, C. D., and Moreu, F. (2021), Automatic evaluation of rebar spacing using LiDAR data. Automation in Construction, Elsevier 131, 103890. https://doi.org/10.1016/j.autcon.2021.103890
- Kardovskyi, Y., and Moon, S. (2021). Artificial intelligence quality inspection of steel bars installation by integrating mask R-CNN and stereo vision. Automation in Construction, Elsevier, 130, 103850. https://doi.org/10.1016/j.autcon.2021.103850
- KD S 3504. (2021), Steel bars for concrete reinforcement.
- Pulcrano, M., et al. (2019), 3D cameras acquisitions for the documentation of cultural heritage, Remote Sensing and Spatial Information Sciences, ISPRS, 42, 639-646.
- Kang, I, S., et al. (2017), Distortion in VR 360 degree panoramic image, Proceedings of the Korean Society of Broadcast Engineers Conference, Seoul, 194-196.
- Matterport. (2017), Available at: https://matterport.com/
- Angeli, Adrien, et al. (2008), Real-time visual loop-closure detection, 2008 IEEE international conference on robotics and automation, IEEE, 1842-1847.
- Shi, G., Xu, X., and Dai, Y. (2013), SIFT feature point matching based on improved RANSAC algorithm, In 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, IEEE, 474-477.
- Kummerle, Rainer, et al. (2011), g2o: A general framework for graph optimization, 2011 IEEE International Conference on Robotics and Automation. IEEE, 3607-3613.
- Grisetti, Giorgio, et al. (2010), A tutorial on graph-based SLAM, IEEE Intelligent Transportation Systems Magazine, IEEE, 2(4) 31-43. https://doi.org/10.1109/MITS.2010.939925
- Gupta, M., Yin, Q., and Nayar, S. K. (2013), Structured light in sunlight, Proceedings of the IEEE International Conference on Computer Vision, 545-552.
- Tareen, S. A. K., and Saleem, Z. (2018), A comparative analysis of sift, surf, kaze, akaze, orb, and brisk, International conference on computing, mathematics and engineering technologies (iCoMET), IEEE, 1-10.