1 |
Girshick, R., Donahue, H., Darrell, T., and Malik, J. (2014). "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation." In CVPR, pp. 1-22
|
2 |
Golparvar-fard, M., and Pena-Mora, F. (2007). "Application of Visualization Techniques for Construction Progress Monitoring." Congress on Computing in Civil Engineering, Proceedings, pp. 216-223.
|
3 |
Golparvar-fard, M., Heydarian, A., and Niebles, J. (2013). "Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers." Advanced Engineering Informatics, 27, pp. 652-663.
DOI
|
4 |
Hamledari, H., McCabe, B., and Davari, S. (2017). "Automated Computer Vision-Based Detection of Components of Under-Construction Indoor Partitions." Automation in Construction, 74, pp. 78-97.
DOI
|
5 |
Hamledari, H., McCabe, B., Davari, S., Shahi, A., Rezazadeh, E., and Flager, F. (2017). "Evaluation of Computer Vision-and 4D BIM-Based Construction Progress Tracking on a UAV Platform." Leadership in Sustainable Infrastructure, pp. 1-10.
|
6 |
Han, K., Lin, J., and Golparvar-fard, M. (2015). "A Formalism for Utilization of Autonomous Vision-Based Systems and Integrated Project Models for Construction Progress Monitoring." Proceedings of the 2015 Conference on Autonomous and Robotic Construction of Infrastructure, pp. 118-131.
|
7 |
He, H., Zhang, X., Ren, S., and Sun, J. (2016). "Deep Residual Learning for Image Recognition." 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778.
|
8 |
He, K., Zhang, X., Ren, S., and Sun, J. (2015). "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition." In ECCV. pp. 1-14.
|
9 |
Hikodukue, K. Python Ni Yoru Scraping and Kinkaigakusho Kaihatsu Technique (2017). Socym Press, Japan.
|
10 |
Ibrahim, Y., Lukins, T., Zhang, X., Trucco, E., and Kaka, A. (2009). "Towards Automated Progress Assessment of Work package Components in Construction Projects Using Computer Vision." Advanced Engineering Informatics, 23, pp. 93-103.
DOI
|
11 |
BuildingSMART, (2018). "Industrial Foundation Classes from BuildingSMART." United States, (Accessed May 1, 2020)
|
12 |
Lecun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). "Gradient-Based Learning Applied to Document Recognition." Proceedings of the IEEE, pp. 1-46.
|
13 |
Kim, S., Kim, Y., Yoou,, J., and Kim, E. (2012). "A framework of the open BIM-based integrated information system for the Korean Traditional House." Journal of Architectural Institute of Korea, 28(9), pp. 13-20.
|
14 |
Kong, J., and Jang, M. (2019). "Association Analysis of Convolution Layer, Kernel and Accuracy in CNN." Journal of the KIECS, 14(6), pp. 1153-1160.
|
15 |
Kropp, C., Koch, C., and Konig, M. (2018). "Interior construction state recognition with 4D BIM registered image sequences." Automation in Construction, 86, pp. 11-32.
DOI
|
16 |
McCulloch, C.E. (1997). "Maximum Likelihood Algorithms for Generalized Linear Mixed Models." Journal of the American Statistical Association, 92, pp. 162-170.
DOI
|
17 |
Memon, Z., Abd.Majid, M., and Mustaffar, M. (2005). "An Automatic Project Progress Monitoring Model by Integrating Auto CAD and Digital Photos." International Conference on Computing in Civil Engineering, pp. 1-13.
|
18 |
Redmon, J., Divvala, S., Girchick, R., and Fahadi, A. (2016). "You Only Look Once: Unified, Real-Time Object Detection." in CVPR, pp. 779-788.
|
19 |
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelow, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). "Going deeper with convolutions." in CVPR, pp. 1-9.
|
20 |
Simonyan, K., and Zisserman, A. (2015). "Very Deep Convolutional Networks for Large-Scale Image Recognition." International Conference on Learning Representations (ICLR), pp. 1-14.
|
21 |
Cho, T. (2018). Deep Learning for Everyone. Seoul: Gilbut Press.
|
22 |
Akhavia, R., and Behzadan, A. (2016). "Smartphone-based construction workers' activity recognition and classification." Automation in Construction, 71, pp. 198-209.
DOI
|
23 |
Azar, R. (2017). "Semantic Annotation of Videos from Equipment-Intensive Construction Operations by Shot Recognition and Probabilistic Reasoning." Journal of Computing in Civil Engineering, 31(5), pp. 04017042-04017042.
DOI
|
24 |
buildingSMART International Modeling Support Group (2009). "IFC 2x Edition 3 Model Implementation Guide." buildingSMART International Modeling Support Group.
|
25 |
Deng, H., Hong, H., Luo, D., Deng, Y., and Su, C. (2020). "Automatic Indoor Construction Process Monitoring for Tiles Based on BIM and Computer Vision." Journal of Construction Engineering and Management, 146(1), DOI: 10.1061/(ASCE)CO.1943-7862.0001744.
|
26 |
Eastman, C., Jeong, Y., Sack, R., and Karner, L. (2009). "Exchange model and exchange object concepts for implementation of national BIM standards." Journal of Computing in Civil Engineering, 24(1), pp. 24-35.
|
27 |
Son, H., Choi, H., Seong, H., and Kim, C. (2019). "Detection of construction workers under varying poses and changing background in image sequences via very deep residual networks." Automation in Construction, 99, pp. 27-38.
DOI
|
28 |
Trucco, E., and Kaka, P. (2004). "A framework for automatic progress assessment on construction sites using computer vision." International Journal of IT in Architecture Engineering and Construction, 2(2), pp. 147-164.
|
29 |
Zhang, X., Bakis, N., Lukins, T., Ibrahim, Y., Wu, S., Kagioglou, M., Aouad, G., Kaka, A., and Trucco, E. (2009). "Automating Progress Measurement of Construction Projects." Automation in Construction, 18, pp. 294-301.
DOI
|
30 |
Ding, L., Fang, W., Luo, H., Love, P., Zhong, B., and Ouyang, X. (2018). "A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory." Automation in Construction, 86, pp. 118-124
DOI
|
31 |
FARO (2010). "FARO Scanner Production Technology." FARO Technologies, United States, (Accessed May 1, 2020)
|
32 |
Fang, W., Ding, L., Luo, H., and Love, D. (2018). "Falls from heights: A computer vision-based approach for safety harness detection." Automation in Construction, 91, pp. 53-61.
DOI
|