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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

A CV-Based Automatic Method of Acquiring and Processing Operation Data on Construction Site

verfasst von : Hui Li, Hongling Guo, Zhihui Zhang

Erschienen in: Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Verlag: Springer Singapore

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Abstract

Image data of construction site is often of large volume and difficult to handle. This paper introduces a computer-vision-based automatic method of acquiring and processing this kind of data. A deep convolutional neural network along with region proposal network is used for on-site object detection including workers, materials and machines, followed by a light-weighed network to determine the real-time interaction between workers and working objects. A practical implication of the two network models and their experimental results is a scenario-based security and productivity management system and its basic structure is also introduced in this paper.

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Vorheriges Kapitel Evaluation of Point Cloud Processing Software for 3D Reconstruction
Nächstes Kapitel Applied Research on BIM+GIS Integration Technology in Subway Project Management
Literatur
1.
Chi, S., & Caldas, C. H. (2011). Automated object identification using optical video cameras on construction sites. Computer-Aided Civil & Infrastructure Engineering, 26(5), 368–380.CrossRef
2.
Giovanni, G., Andrea, P., & Rita, C. (2011). Contextual information and covariance descriptors for people surveillance: An application for safety of construction workers. EURASIP Journal on Image and Video Processing, 2011(1), 1–16.CrossRef
3.
Park, M. W., & Brilakis, I. (2012). Construction worker detection in video frames for initializing vision trackers. Automation in Construction, 28(15), 15–25.CrossRef
4.
Heydarian, A., Golparvar-Fard, M., & Niebles, J. C. (2012). Automated visual recognition of construction equipment actions using spatio-temporal features and multiple binary support vector machines. In Construction research congress 2012: Construction challenges in a flat world (pp. 889–898).
5.
Rezazadeh Azar, E., & McCabe, B. (2011). Automated visual recognition of dump trucks in construction videos. Journal of Computing in Civil Engineering, 26(6), 769–781.CrossRef
6.
Yang, W., Li, D., Sun, D., & Liao, Q. (2014, November). Hydraulic excavators recognition based on inverse” v” feature of mechanical arm. In Chinese Conference on Pattern Recognition (pp. 536–544). Berlin, Heidelberg: Springer.
7.
Ren, X., Zhu, Z., Germain, C., Dean, B., & Chen, Z. (2015). A case study of construction equipment recognition from time-lapse site videos under low ambient illuminations. In Computing in civil engineering 2015 (pp. 82–89).
8.
Memarzadeh, M., Golparvar-Fard, M., & 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, 32, 24–37.CrossRef
9.
Fang, W., Ding, L., Zhong, B., Love, P. E., & Luo, H. (2018). Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach. Advanced Engineering Informatics, 37, 139–149.CrossRef
10.
Luo, H., Xiong, C., Fang, W., Love, P. E., Zhang, B., & Ouyang, X. (2018). Convolutional neural networks: Computer vision-based workforce activity assessment in construction. Automation in Construction, 94, 282–289.CrossRef
11.
Luo, X., Li, H., Cao, D., Dai, F., Seo, J., & Lee, S. (2018). Recognizing diverse construction activities in site images via relevance networks of construction-related objects detected by convolutional neural networks. Journal of Computing in Civil Engineering, 32(3).
12.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324.CrossRef
13.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
14.
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 580–587).
15.
He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1904–1916.CrossRef
16.
Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1440–1448).
17.
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91–99).
18.
He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017, October). Mask r-cnn. In 2017 IEEE International Conference on Computer Vision (ICCV) (pp. 2980–2988). IEEE.
19.
Chao, Y. W., Wang, Z., He, Y., Wang, J., & Deng, J. (2015). Hico: A benchmark for recognizing human-object interactions in images. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1017–1025).
20.
Gkioxari, G., Girshick, R., & Malik, J. (2015). Contextual action recognition with r*cnn. International Journal of Cancer Journal International Du Cancer, 40(1), 1080–1088.
Metadaten
Titel
A CV-Based Automatic Method of Acquiring and Processing Operation Data on Construction Site
verfasst von
Hui Li
Hongling Guo
Zhihui Zhang
Copyright-Jahr
2021
Verlag
Springer Singapore
Buch
Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Print ISBN: 978-981-15-8891-4

Electronic ISBN: 978-981-15-8892-1

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-981-15-8892-1_90

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