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http://dx.doi.org/10.5659/JAIK.2022.38.4.217

Proposal and Verification of the Faster R-CNN Regarding the Worker and Machine Interference Scope Detection Model to Prevent On-site Safety Accidents  

Wang, Zepu (Dept. of Architectural Engineering, Hanyang University)
Kim, Jang-Soon (Architectural Engineering, Hanyang University)
Ham, Nam-Hyuk (Dept. of Digital Architectural and Urban Engineering, Hanyang Cyber University)
Kim, Jae-Jun (Dept. of Architectural Engineering, Hanyang University)
Publication Information
Journal of the Architectural Institute of Korea / v.38, no.4, 2022 , pp. 217-228 More about this Journal
Abstract
Safety management of construction projects have a significant impact on the construction project's schedule and the control carried out on site. Current site safety monitoring methods are highly dependent on manual labor; human errors can occur through missing content. This study aims to resolve these issues by applying machine learning visual detection algorithms to identify unsafe behaviors of workers at construction sites, to enhance external monitoring of workers and to relatively reduce the occurrence of safety accidents. A proposed method combines an object detection algorithm and spatial localization relationship definition. Only the machinery and workers at the construction site need to be accurately detected and the definition of spatial location relationship can be used to identify dangerous behaviors. A monitoring network framework suitable for this study was constructed with the environmental characteristics and image features of a construction site. The machines and workers were detected from construction images based on the Faster R-CNN algorithm for a computer to obtain the visual detection data from the construction site. Three spatial concepts were defined to determine the position relationships of machines and workers in these images. The detected location information of machines and workers at the construction site were combined and presented in a visualized form. Based on the results of this research, it confirmed that the method and performance were suitable for construction site safety management, which is expected to contribute to the speed, level of accuracy and risk warning with the application of automated progress monitoring methods.
Keywords
Machine Learning; Construction Safety Management; Deep Learning; Faster R-CNN; Visual Inspection Model; Image Analysis;
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Times Cited By KSCI : 5  (Citation Analysis)
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1 Tixier, A. J. P., Hallowell, M. R., Rajagopalan, B., & Bowman, D. (2016). Application of machine learning to construction injury prediction. Automation in construction, 69, 102-114.   DOI
2 Park, C. S., & Kim, H. J. (2013). A framework for construction safety management and visualization system. Automation in Construction, 33, 95-103.   DOI
3 Li, R. Y. M., & Ng, D. P. L. (2017). Wearable robotics, industrial robots and construction worker's safety and health. In International Conference on Applied Human Factors and Ergonomics (pp. 31-36). Springer, Cham.
4 Liu, H., & Tian, G. (2019). Building engineering safety risk assessment and early warning mechanism construction based on distributed machine learning algorithm. Safety Science, 120, 764-771.   DOI
5 Lu, W., Huang, G. Q., & Li, H. (2011). Scenarios for applying RFID technology in construction project management. Automation in construction, 20(2), 101-106.   DOI
6 Lee, J. M., Park, S. H., Cho, S. H., & Kim, J. H. (2021). Comparison of Models to Forecast Real Estates Index Introducing Machine Learning. Journal of the Architectural Institute of Korea, 37(1), 191-199.   DOI
7 Chakkravarthy, R. (2019). Artificial intelligence for construction safety. Professional Safety, 64(1), 46.
8 Chian, E., Fang, W., Goh, Y. M., & Tian, J. (2021). Computer vision approaches for detecting missing barricades. Automation in Construction, 131, 103862.   DOI
9 Gheisari, M., & Esmaeili, B. (2016). Unmanned aerial systems (UAS) for construction safety applications. In Construction Research Congress 2016 (pp. 2642-2650).
10 Cobo, L. C., Isbell Jr, C. L., & Thomaz, A. L. (2013). Object focused q-learning for autonomous agents. Georgia Institute of Technology.
11 Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
12 Shao, B., Hu, Z., Liu, Q., Chen, S., & He, W. (2019). Fatal accident patterns of building construction activities in China. Safety science, 111, 253-263.   DOI
13 Shin, J., Kim, Y., & Kim, C. (2021). The perception of occupational safety and health (OSH) regulation and innovation efficiency in the construction industry: evidence from South Korea. International journal of environmental research and public health, 18(5), 2334.   DOI
14 Seong, H., Son, H., & Kim, C. (2018). A comparative study of machine learning classification for color-based safety vest detection on construction-site images. KSCE Journal of Civil Engineering, 22(11), 4254-4262.   DOI
15 Tian, S. C., Li, H. X., & Wang, L. (2006). Three types hazard theory and prevention of coalmine accidents. Meitan Xuebao, 31(6), 706-710.
16 Wu, L., Mokhtari, S., Nazef, A., Nam, B., & Yun, H. B. (2016). Improvement of crack-detection accuracy using a novel crack defragmentation technique in image-based road assessment. Journal of Computing in Civil Engineering, 30(1), 04014118.   DOI
17 Xu, Y., Zhou, Y., Sekula, P., & Ding, L. (2021). Machine learning in construction: From shallow to deep learning. Developments in the Built Environment, 6, 100045.   DOI
18 Poh, C. Q., Ubeynarayana, C. U., & Goh, Y. M. (2018). Safety leading indicators for construction sites: A machine learning approach. Automation in construction, 93, 375-386.   DOI
19 Na, S., Xumin, L., & Yong, G. (2010). Research on k-means clustering algorithm: An improved k-means clustering algorithm. In 2010 Third International Symposium on intelligent information technology and security informatics (pp. 63-67). Ieee.
20 O'Byrne, M., Schoefs, F., Ghosh, B., & Pakrashi, V. (2013). Texture analysis based damage detection of ageing infrastructural elements. Computer-Aided Civil and Infrastructure Engineering, 28(3), 162-177.   DOI
21 Peng, X., & Schmid, C. (2016). Multi-region two-stream R-CNN for action detection. In European conference on computer vision (pp. 744-759). Springer, Cham.
22 Perlman, A., Sacks, R., & Barak, R. (2014). Hazard recognition and risk perception in construction. Safety science, 64, 22-31.   DOI
23 Rho, J., Park, M., & Lee, H. S. (2020). Automated Construction Progress Management Using Computer Vision-based CNN Model and BIM. Korean Journal of Construction Engineering and Management, 21(5), 11-19.   DOI
24 Gong, J., & Caldas, C. H. (2010). Computer vision-based video interpretation model for automated productivity analysis of construction operations. Journal of Computing in Civil Engineering, 24(3), 252-263.   DOI
25 Han, G., Oh, T. M., Kim, H., Song, K. I., Kim, Y., & Kwon, T. H. (2019). Determination of Crack Signals Using the Deep Learning Technique Based on a 1D Convolutional Neural Network for Smart Detection of Structural Damage Cracking. Journal of the Korean Society of Hazard Mitigation, 19(4), 1-7.
26 Kang, K., & Ryu, H. (2019). Predicting types of occupational accidents at construction sites in Korea using random forest model. Safety Science, 120, 226-236.   DOI
27 Khosravi, Y., Asilian-Mahabadi, H., Hajizadeh, E., Hassanzadeh-Rangi, N., Bastani, H., & Behzadan, A. H. (2014). Factors influencing unsafe behaviors and accidents on construction sites: A review. International journal of occupational safety and ergonomics, 20(1), 111-125.   DOI
28 Sakhakarmi, S., Park, J., & Cho, C. (2019). Enhanced machine learning classification accuracy for scaffolding safety using increased features. Journal of construction engineering and management, 145(2), pp. 04018133.   DOI
29 Goertzel, B. (2007). Artificial general intelligence (Vol. 2). C. Pennachin (Ed.). New York: Springer.
30 Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
31 Akinlolu, M., Haupt, T. C., Edwards, D. J., & Simpeh, F. (2020). A bibliometric review of the status and emerging research trends in construction safety management technologies. International Journal of Construction Management, 1-13.
32 Gu, H. M., Seo, J. H., & Choo, S. Y. (2019). A Development of Facade Dataset Construction Technology Using Deep Learning-based Automatic Image Labeling. Journal of the Architectural Institute of Korea Planning & Design, 35(12), 43-53.   DOI
33 Kymmell, W. (2008). Building information modeling: Planning and managing construction projects with 4D CAD and simulations (McGraw-Hill construction series). McGraw-Hill Education.
34 Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154-171.   DOI
35 Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision (pp. 818-833). Springer, Cham.
36 Rubaiyat, A. H., Toma, T. T., Kalantari-Khandani, M., Rahman, S. A., Chen, L., Ye, Y., & Pan, C. S. (2016). Automatic detection of helmet uses for construction safety. In 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW) (pp. 135-142). IEEE.
37 Bansal, V. K. (2011). Application of geographic information systems in construction safety planning. International Journal of Project Management, 29(1), 66-77.   DOI
38 Han, J., Zhang, D., Cheng, G., Liu, N., & Xu, D. (2018). Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Processing Magazine, 35(1), 84-100.   DOI
39 Heinrich, H. W. (1980). Industrial Prevention: A Safety management Approach.
40 Klein, J. A. (2009). Two centuries of process safety at DuPont. Process Safety Progress, 28(2), 114-122.   DOI
41 Konstantinou, E., & Brilakis, I. (2018). Matching construction workers across views for automated 3D vision tracking on-site. Journal of Construction Engineering and Management, 144(7), 04018061.   DOI
42 Lee, J. H., Lee, R. W., Hong, S. T., & Kim, Y. G. (2020). Image Processing System based on Deep Learning for Safety of Heat Treatment Equipment. The Journal of the Institute of Internet, Broadcasting and Communication, 20(6), 77-83.   DOI
43 Lee, H. S., Lee, K. P., Park, M. S., Kim, H. S., & Lee, S. B. (2009). A Construction safety management system based on Building Information Modeling and Real-time Locating System. Korean Journal of Construction Engineering and Management, 10(6), 135-145.
44 Roberts, D., Bretl, T., & Golparvar-Fard, M. (2017). Detecting and classifying cranes using camera-equipped UAVs for monitoring crane-related safety hazards. In Computing in Civil Engineering 2017 (pp. 442-449).
45 Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
46 Baker, H., Hallowell, M. R., & Tixier, A. J. P. (2020). AI-based prediction of independent construction safety outcomes from universal attributes. Automation in Construction, 118, 103146.   DOI
47 Carbonari, A., Giretti, A., & Naticchia, B. (2011). A proactive system for real-time safety management in construction sites. Automation in construction, 20(6), 686-698.   DOI
48 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Machine learning basics. Deep learning, 1(7), 98-164.
49 Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T. M., & An, W. (2018). Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Automation in Construction, 85, 1-9.   DOI
50 Ding, L., Fang, W., Luo, H., Love, P. E., Zhong, B., & 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, 118-124.   DOI
51 Adillah, S. U., & Purnawan, A. (2020). Analysis of the Fulfillment of Labor Social Security as a Work of Legal Protection. Journal of Morality and Legal Culture, 1(1), 32-37.   DOI
52 Kolar, Z., Chen, H., & Luo, X. (2018). Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images. Automation in Construction, 89, 58-70.   DOI
53 Zhu, R., Hu, X., Hou, J., & Li, X. (2021). Application of machine learning techniques for predicting the consequences of construction accidents in China. Process Safety and Environmental Protection, 145, 293-302.   DOI
54 Tang, S., & Golparvar-Fard, M. (2021). Machine Learning-Based Risk Analysis for Construction Worker Safety from Ubiquitous Site Photos and Videos. Journal of Computing in Civil Engineering, 35(6), pp. 04021020.   DOI