1 |
The Korea Occupational Safety and Health Agency (KOSHA), Industrial Accident Statistics, 2015-2017.
|
2 |
M. W. Park, E. Palinginis and I. Brilakis, "Detection of Construction Workers in Video Frames for Automatic Initialization of Vision Trackers", Construction Research Congress, pp. 940-949, 2012.
|
3 |
M. W. Park, N. Elsafty and Z. Zhu, "Hardhat-wearing Detection for Enhancing on-site Safety of Construction Workers", Journal of Construction Engineering and Management, Vol. 141, No. 9, 2015.
|
4 |
A. H. M. Rubaiyat, T. T. Toma, M. K. Khandani, S. A. Rahman, L. Chen and Y. Ye, C. S. Pan, "Automatic Detection of Helmet uses for Construction Safety", 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops(WIW). IEEE, pp. 135-142, 2016.
|
5 |
A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
|
6 |
S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks", Advances in Neural Information Processing Systems, pp. 91-99, 2015.
|
7 |
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
|
8 |
K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.
|
9 |
Q. Fang, H. Li, X. Luo, L. Ding, T. M. Rose, W. An and Y. Yu, "A Deep Learning-based Method for Detecting Non-certified Work on Construction Sites", Advanced Engineering Informatics, Vol. 35, pp. 56-68, 2018.
DOI
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