Mobile QR Code QR CODE

Journal of the Korea Concrete Institute

J Korea Inst. Struct. Maint. Insp.
  • Indexed by
  • Korea Citation Index (KCI)

Journal Search

  1. Home
  2. All Issues
  3. 2021-08 (Vol.25 No.4)
  4. 10.11112/jksmi.2021.25.4.65
XML PDF INFO REF

References

1 
Sanpei, T., and Mizoguchi, T. (2018), Fundamental Study for Real-Time Detection of Sudden Displacement by High-Speed Laser Scanner, Journal of Structural Integrity and Maintenance, 3(4), 227-232.DOI
2 
Yamaguchi, T., Nakamura, S., Saegusa, R., and Hashimoto, S. (2008), Image‐Based Crack Detection for Real Concrete Surfaces, IEEJ Transactions on Electrical and Electronic Engineering, Wiley Online Library, 3(1), 128-135.DOI
3 
Yu, S. N., Jang, J. H., and Han, C. S. (2007), Auto Inspection System Using a Mobile Robot for Detecting Concrete Cracks in a Tunnel, Automation in Construction, Elsevier, 16(3), 255-261.DOI
4 
Lee, S. H., Shin, K. J., Kim, H. J., Kim, S. Y., Yoo, C. H., and Eom S. G. (2019), Introduction of Tunnel Crack Measurement Technology Using Image Scanning, Journal of Korean Society of Steel Construction, 31(6), 42-48.URL
5 
Song, Q., Wu, Y., Xin, X., Yang, L., Yang, M., Chen, H., Liu, C., HU, M., CHAI, X., and Li, J. (2019), Real-time Tunnel Crack Analysis System via Deep Learning. IEEE Access, IEEE, 7, 64186-64197.DOI
6 
Li, G., Ma, B., He, S., Ren, X., and Liu, Q. (2020), Automatic Tunnel Crack Detection based on U-Net and a Convolutional Neural Network with Alternately Updated Clique. Sensors, MDPI, 20(3), 717.DOI
7 
Ronneberger, O., Fischer, P., and Brox, T. (2015), U-net: Convolutional Networks for Biomedical Image Segmentation, International Conference on Medical Image Computing and Computer-assisted Intervention, Springer, Berlin, 234-241.DOI
8 
Choi, Y., Kim, J., Cho, H., and Lee, C. (2019) Asphalt Concrete Pavement Surface Crack Detection using Convolutional Neural Network, Journal of the Korea Institute for Structural Maintenance and Inspection, 23(6), 38-44.URL
9 
Kim B., and Cho, S. (2019), Image-based Concrete Crack Assessment using Mask and Region-based Convolutional Neural Network, Structural Control and Health Monitoring, Wiley, 26(8), e2381(1-15).DOI
10 
Kim B., and Cho, S. (2020), Automated Multiple Concrete Damage Detection Using Instance Segmentation Deep Learning Model, Applied Sciences, MDPI, 9(20), 4444(1-14).DOI
11 
Jang, K., An, Y.-K., Kim, S., and Cho, S. (2021) Automated Crack Evaluation of a High‐Rise Bridge Pier Using a Ring‐Type Climbing Robot, Computer-aided Civil and Infrastructure Engineering, Wiley, 26, 14-29.DOI
12 
He, K., Gkioxari, G., Dollár, P., and Girshick, R. (2017), Mask r-cnn. Proceedings of the IEEE international conference on computer vision, IEEE, 2961-2969.URL
13 
Cai, Z., and Vasconcelos, N. (2018), Cascade r-cnn: Delving into High Quality Object Detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, 6154-6162.URL
14 
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C. L. (2014), Microsoft Coco: Common Objects in Context, European Conference on Computer Vision, Springer, Berlin, 740-755.DOI
15 
Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Xu, J., Zhang, Z., Cheng, D., Zhu, C., Cheng, T., Zhao, Q., Li, B., Lu, X., Zhu, R., Wu, Y., Dai, J., Wang, J., Shi, J., Ouyang, W., Loy, C. C., and Lin, D. (2019), MMDetection: Open Mmlab Detection Toolbox and Benchmark, ArXiv Preprint, ArXiv, 1906.07155.DOI
16 
Robbins, H., and Monro, S. (1951), A Stochastic Approximation Method, The Annals of Mathematical Statistics, Institute of Mathematical Statistics, 400-407.URL
17 
Pascanu, R., Mikolov, T., and Bengio, Y. (2013), On the Difficulty of Training Recurrent Neural Networks, International Conference on Machine Learning, PMLR, 1310-1318.URL
18 
Loshchilov, I., and Hutter, F. (2016), SGDR: Stochastic Gradient Descent with Warm Restarts, ArXiv Preprint, arXiv, 1608.03983.DOI
19 
Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., and Fei-Fei, L. (2009). Imagenet: A Large-Scale Hierarchical Image Database, 2009 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, 248-255.DOI
20 
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K. (2017), Aggregated Residual Transformations for Deep Neural Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Piscataway, 1492-1500.URL