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2019 Vol.39, Issue 3 Preview Page

June 2019. pp. 399-407
Abstract


References
1 

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S, Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y. and Zheng, X. (2016). "Tensorflow: a system for large-scale machine learning." USENIX Symposium on Operating Systems Design and Implementation, Vol. 16, pp. 265-283.

2 

Dai, J., Li, Y., He, K. and Sun, J. (2016). "R-fcn: Object detection via region-based fully convolutional networks." Advances in Neural Information Processing Systems, pp. 379-387.

3 

Deng, J., Dong, W., Socher, R., Li, L. J., Li, K. and Fei-Fei, L. (2009). "Imagenet: A large-scale hierarchical image database." Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 248-255.

10.1109/CVPR.2009.5206848
4 

Everingham, M., Van Gool, L., Williams, C. K., Winn, J. and Zisserman, A. (2010). "The pascal visual object classes (voc) challenge." International Journal of Computer Vision, Vol. 88, No. 2, pp. 303-338.

10.1007/s11263-009-0275-4
5 

Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T. M. and An, W. (2018). "Detecting non-hardhat-use by a deep learning method from far-field surveillance videos." Automation in Construction, Vol. 85, pp. 1-9.

10.1016/j.autcon.2017.09.018
6 

Girshick, R. (2015). "Fast r-cnn." Proc. of the IEEE International Conference on Computer Vision, IEEE, pp. 1440-1448.

10.1109/ICCV.2015.169
7 

Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014). "Rich feature hierarchies for accurate object detection and semantic segmentation." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 580-587.

10.1109/CVPR.2014.81
8 

He, K., Zhang, X., Ren, S. and Sun, J. (2016). "Deep residual learning for image recognition." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 770-778.

10.1109/CVPR.2016.90
9 

Illingworth, J. and Kittler, J. (1988). "A survey of the hough transform." Computer Vision, Graphics, and Image Processing, Vol. 44, No. 1, pp. 87-116.

10.1016/S0734-189X(88)80033-1
10 

Kim, H., Kim, H., Hong, Y. W. and Byun, H. (2017). "Detecting construction equipment using a region-based fully convolutional network and transfer learning." Journal of Computing in Civil Engineering, ASCE, Vol. 32, No. 2, pp. 04017082.

10.1061/(ASCE)CP.1943-5487.0000731
11 

Li, Z., Chen, Y., Yu, G. and Deng, Y. (2018). "R-FCN++: Towards accurate region-based fully convolutional networks for object detection." Proc. of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans.

12 

Lin, T. Y., Dollár, P., Girshick, R. B., He, K., Hariharan, B. and Belongie, S. J. (2017). "Feature pyramid networks for object detection." CVPR, Vol. 1, No. 2, p. 4.

10.1109/CVPR.2017.106
13 

Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C. L. (2014). "Microsoft coco: Common objects in context." Proc. of European Conference on Computer Vision, Springer, Cham, pp. 740-755.

10.1007/978-3-319-10602-1_48
14 

Liu, B., Wei, Y., Zhang, Y. and Yang, Q. (2017). "Deep neural networks for high dimension, low sample size data." Proc. of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, Melbourne, pp. 2287-2293.

10.24963/ijcai.2017/318
15 

Liu, W. and Zeng, K. (2018). "SparseNet: A sparse densenet for image classification." arXiv preprint arXiv:1804.05340.

16 

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y. and Berg, A. C. (2016). "Ssd: Single shot multibox detector." Proc. of European Conference on Computer Vision, Springer, Cham, pp. 21-37.

10.1007/978-3-319-46448-0_2
17 

Maas, A. L., Hannun, A. Y. and Ng, A. Y. (2013). "Rectifier nonlinearities improve neural network acoustic models." Proc. of ICML, Vol. 30, No. 1, p. 3.

18 

Memarzadeh, M., Golparvar-Fard, M. and Niebles, J. C. (2013). "Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors." Automation in Construction, Vol. 32, pp. 24-37.

10.1016/j.autcon.2012.12.002
19 

Mistry, J., Misraa, A. K., Agarwal, M., Vyas, A., Chudasama, V. M. and Upla, K. P. (2017). "An automatic detection of helmeted and non-helmeted motorcyclist with license plate extraction using convolutional neural network." Proc. of 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE, pp. 1-6.

10.1109/IPTA.2017.8310092
20 

Nasrabadi, N. M. (2007). "Pattern recognition and machine learning." Journal of electronic imaging, Vol. 16, No. 4, p. 049901.

10.1117/1.2819119
21 

Occupational Safety and Health Research Institute (OSHRI) (2016). Cause of Industrial Accident in 2014, OSHRI Research Report (in Korean).

22 

Redmon, J. and Farhadi, A. (2017). "YOLO9000: better, faster, stronger." arXiv preprint.

10.1109/CVPR.2017.690
23 

Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). "You only look once: Unified, real-time object detection." Proc. of IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 779-788.

10.1109/CVPR.2016.91
24 

Ren, S., He, K., Girshick, R. and Sun, J. (2015). "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in Neural Information Processing Systems, pp. 91-99.

25 

Rubaiyat, A. H., Toma, T. T., Kalantari-Khandani, M., Rahman, S. A., Chen, L., Ye, Y. and Pan, C. S. (2016). "Automatic detection of helmet uses for construction safety." Proc. of 2016 IEEE/WIC/ ACM International Conference on Web Intelligence Workshops (WIW), IEEE, pp. 135-142.

10.1109/WIW.2016.045
26 

Shao, L., Zhu, F. and Li, X. (2015). "Transfer learning for visual categorization: A survey." IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 5, pp. 1019-1034.

10.1109/TNNLS.2014.233090025014970
27 

Silva, R., Aires, K., Santos, T., Abdala, K., Veras, R. and Soares, A. (2013). "Automatic detection of motorcyclists without helmet." Proc. of Computing Conference (CLEI), 2013 XXXIX Latin American, IEEE, pp. 1-7.

10.1109/CLEI.2013.6670613
28 

Uijlings, J. R., Van De Sande, K. E., Gevers, T. and Smeulders, A. W. (2013). "Selective search for object recognition." International Journal of Computer Vision, Vol. 104, No. 2, pp. 154-171.

10.1007/s11263-013-0620-5
29 

Vishnu, C., Singh, D., Mohan, C. K. and Babu, S. (2017). "Detection of motorcyclists without helmet in videos using convolutional neural network." Proc. of 2017 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 3036-3041.

10.1109/IJCNN.2017.7966233
30 

Weiss, K., Khoshgoftaar, T. M. and Wang, D. (2016). "A survey of transfer learning." Journal of Big Data, Vol. 3, No. 1, p. 9.

10.1186/s40537-016-0043-6
31 

Wen, C. Y., Chiu, S. H., Liaw, J. J. and Lu, C. P. (2003). "The safety helmet detection for ATM's surveillance system via the modified Hough transform." Proc. of the IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, IEEE, pp. 364-369.

32 

You, H. J., You, Y. T. and Kang, K. S. (2017). "A study of the efficiency improvement of the safety management personnel system in apartment construction site." Korea Safety Management & Science Korea Safety Management & Science, Vol. 19, No. 1, pp. 87-94 (in Korean).

10.12812/ksms.2017.19.1.87
33 

Yuen, H., Princen, J., Illingworth, J. and Kittler, J. (1990). "Comparative study of Hough transform methods for circle finding." Image and Vision Computing, Vol. 8, No. 1, pp. 71-77.

10.1016/0262-8856(90)90059-E
34 

Zhu, Y. and Newsam, S. (2017). "Densenet for dense flow." Proc. of 2017 IEEE International Conference on Image Processing (ICIP), IEEE. pp. 790-794.

10.1109/ICIP.2017.8296389
Information
  • Publisher :Korean Society of Civil Engineers
  • Publisher(Ko) :대한토목학회
  • Journal Title :JOURNAL OF THE KOREAN SOCIETY OF CIVIL ENGINEERS
  • Journal Title(Ko) :대한토목학회 논문집
  • Volume : 39
  • No :3
  • Pages :399-407
  • Received Date :2019. 01. 08
  • Revised Date :2019. 04. 08
  • Accepted Date : 2019. 04. 10