Utilizing Computer Vision and Fuzzy Inference to Evaluate Level of Collision Safety for Workers and Equipment in a Dynamic Environment
Publication: Journal of Construction Engineering and Management
Volume 146, Issue 6
Abstract
The construction industry is facing unique problems in accident prevention. The existing management method for detecting workers’ unsafe behaviors and unsafe states of objects relies primarily on manual monitoring, which does not only consume large amounts of time and money but also cannot cover all workers in the entire construction site. Meanwhile, the workers’ perception of being at risk of injury decreases when they are concentrated in a crowded and noisy environment. In this case, it is difficult for them to take essential measures to protect themselves in the face of danger. In view of the aforementioned issues, this study proposes a method of evaluating the collision safety level of construction workers based on computer vision and fuzzy inference. Specifically, the proposed model works via two modules: vision extraction and safety assessment. The vision extraction module identifies construction workers and equipment through computer vision; centroid pixel coordinates and crowdedness are then extracted from a detection box. Afterward, the spatial relationship between moving devices and workers is calculated by a pixel calibration process. In the safety assessment module, the collected status information is analyzed by evaluating the safety level of each worker and conducting accident prevention through a fuzzy inference system. The safety level, which indicates the comprehensive risk of collision between workers and equipment in a particular dynamic environment, will be displayed numerically, breaking through the limitations of conventional qualitative evaluation. Field experiments validate the feasibility of the proposed method of informing workers about potential danger situations in an objective way. Moreover, by setting a safety-level threshold, the onsite safety management personnel can take corresponding measures to avoid collision accidents when the worker’s safety level is lower than the threshold.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data-sharing policy can be found here: http://ascelibrary.org/doi/10.1061/(ASCE)CO.1943-7862.0001263.
Acknowledgments
The presented work is supported by the Fundamental Research Funds for the Central Universities of China (No. DUT18JC44) and the Natural Science Foundation of Liao Ning Province 2019 (No. 2019-MS-052).
References
Azar, E. R., and B. McCabe. 2012. “Automated visual recognition of dump trucks in construction videos.” J. Comput. Civ. Eng. 26 (6): 769–781. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000179.
Chen, B., Z. H. Tian, Z. S. Chen, Z. C. Zhang, and W. J. Sun. 2018. “Structural safety evaluation of in-service tunnels using an adaptive neuro-fuzzy inference system.” J. Aerosp. Eng. 31 (5): 04018073. https://doi.org/10.1061/(ASCE)AS.1943-5525.0000883.
Chi, S., and C. H. Caldas. 2012. “Image-based safety assessment: Automated spatial safety risk identification of earthmoving and surface mining activities.” J. Constr. Eng. Mater. 138 (3): 341–351. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000438.
Ding, L. Y., W. L. Fang, H. B. Luo, P. Love, B. T. Zhong, and X. Ouyang. 2018. “A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory.” Autom. Constr. 86 (Feb): 118–124. https://doi.org/10.1016/j.autcon.2017.11.002.
Fang, Q., H. Li, X. C. Luo, L. Y. Ding, H. B. Luo, T. M. Rose, and W. P. An. 2018a. “Detecting non-hardhat-use by a deep learning method from far -field surveillance videos.” Automat. Constr. 85 (Jan): 1–9. https://doi.org/10.1016/j.autcon.2017.09.018.
Fang, W. L., L. Y. Ding, B. T. Zhong, P. Love, and H. B. Luo. 2018b. “Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach.” Adv. Eng. Inf. 37 (Aug): 139–149. https://doi.org/10.1016/j.aei.2018.05.003.
Fernandez, M. D., S. Quintana, N. Chavarria, and J. A. Ballesteros. 2009. “Noise exposure of workers of the construction sector.” Appl. Acoust. 70 (5): 753–760. https://doi.org/10.1016/j.apacoust.2008.07.014.
Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation, 580–587. New York: IEEE.
Goh, Y. M., and D. K. H. Chua. 2010. “Case-based reasoning approach to construction safety hazard identification: Adaptation and utilization.” J. Constr. Eng. Manage. 136 (2): 170–178. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000116.
Hinze, J., X. Y. Huang, and L. Terry. 2005. “The nature of struck-by accidents.” J. Constr. Eng. Manage. 131 (2): 262–268. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(262).
Jablonski, K., and T. Grychowski. 2018. “Fuzzy inference system for the assessment of indoor environmental quality in a room.” Indoor Built Environ. 27 (10): 1415–1430. https://doi.org/10.1177/1420326X17728097.
Kim, H., K. Kim, and H. Kim. 2016a. “Data-driven scene parsing method for recognizing construction site objects in the whole image.” Autom. Constr. 71 (2): 271–282. https://doi.org/10.1016/j.autcon.2016.08.018.
Kim, H., K. Kim, and H. Kim. 2016b. “Vision-based object-centric safety assessment using fuzzy inference: Monitoring struck-by accidents with moving objects.” J. Comput. Civ. Eng. 30 (4): 04015075. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000562.
Kim, K., H. Kim, and H. Kim. 2017. “Image-based construction hazard avoidance system using augmented reality in wearable device.” Autom. Constr. 83 (Nov): 390–403. https://doi.org/10.1016/j.autcon.2017.06.014.
Kolar, Z., H. N. Chen, and X. W. Luo. 2018. “Transfer learning and deep convolutional neural networks for safety guardrail detection in 2D images.” Autom. Constr. 89 (May): 58–70. https://doi.org/10.1016/j.autcon.2018.01.003.
Konstantinou, E., J. Lasenby, and I. Brilakis. 2019. “Adaptive computer vision-based 2D tracking of workers in complex environments.” Autom. Constr. 103 (Jul): 168–184. https://doi.org/10.1016/j.autcon.2019.01.018.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2017. “ImageNet classification with deep convolutional neural networks.” Commun. ACM 60 (6): 84–90. https://doi.org/10.1145/3065386.
Luo, X. C., H. Li, T. Huang, and T. Rose. 2016. “A field experiment of workers’ responses to proximity warnings of static safety hazards on construction sites.” Saf. Sci. 84 (Apr): 216–224. https://doi.org/10.1016/j.ssci.2015.12.026.
Man, S. S., A. Chan, and H. M. Wong. 2017. “Risk-taking behaviors of Hong Kong construction workers: A thematic study.” Saf. Sci. 98 (Oct): 25–36. https://doi.org/10.1016/j.ssci.2017.05.004.
Man-Woo, P., E. Palinginis, and I. Brilakis. 2012. “Detection of construction workers in video frames for automatic initialization of vision trackers.” In Proc., 2012 Construction Research Congress, 940–949. Reston, VA: ASCE.
Memarzadeh, M., M. Golparvar-Fard, and J. C. Niebles. 2013. “Automated 2D detection of construction equipment and workers from site video streams using histograms of oriented gradients and colors.” Autom. Constr. 32 (Jul): 24–37. https://doi.org/10.1016/j.autcon.2012.12.002.
Montaser, A., and O. Moselhi. 2014. “RFID indoor location identification for construction projects.” Autom. Constr. 39 (Apr): 167–179. https://doi.org/10.1016/j.autcon.2013.06.012.
Naticchia, B., M. Vaccarini, and A. Carbonari. 2013. “A monitoring system for real-time interference control on large construction sites.” Autom. Constr. 29 (Jan): 148–160. https://doi.org/10.1016/j.autcon.2012.09.016.
Park, M. W., and I. Brilakis. 2012. “Enhancement of construction equipment detection in video frames by combining with tracking.” In Proc., 2012 ASCE Int. Conf. on Computing in Civil Engineering, 421–428. Reston, VA: ASCE. https://doi.org/10.1061/9780784412343.0053.
Pradhananga, N., and J. Teizer. 2013. “Automatic spatio-temporal analysis of construction site equipment operations using GPS data.” Autom. Constr. 29 (Jan): 107–122. https://doi.org/10.1016/j.autcon.2012.09.004.
Ren, S. Q., K. M. He, R. Girshick, and J. Sun. 2017. “Faster R-CNN: Towards real-time object detection with region proposal networks.” IEEE Trans. Pattern Anal. Mach. Intell. 39 (6): 1137–1149. https://doi.org/10.1109/TPAMI.2016.2577031.
Seo, J., S. Han, S. Lee, and H. Kim. 2015. “Computer vision techniques for construction safety and health monitoring.” Adv. Eng. Inf. 29 (2): 239–251. https://doi.org/10.1016/j.aei.2015.02.001.
Su, X., S. Li, C. X. Yuan, H. B. Cai, and V. R. Kamat. 2014. “Enhanced boundary condition-based approach for construction location sensing using RFID and RTK GPS.” J. Constr. Eng. Mater. 140 (10): 04014048. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000889.
Teizer, J., D. Lao, and M. Sofer. 2007. “Rapid automated monitoring of construction site activities using ultra-wideband.” In Proc., Int. Symp. on Automation and Robotics in Construction, 23–28. Kochi, India: International Association for Automation and Robotics in Construction.
Wang, J., and S. N. Razavi. 2016. “Low false alarm rate model for unsafe-proximity detection in construction.” J. Comput. Civ. Eng. 30 (2): 04015005. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000470.
Wang, J., and S. N. Razavi. 2018. “Spatiotemporal network-based model for dynamic risk analysis on struck-by-equipment hazard.” J. Comput. Civ. Eng. 32 (2): 04017089. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000732.
Wu, W. W., H. J. Yang, Q. M. Li, and D. Chew. 2013. “An integrated information management model for proactive prevention of struck-by-falling-object accidents on construction sites.” Autom. Constr. 34 (SI): 67–74. https://doi.org/10.1016/j.autcon.2012.10.010.
Zhang, M. Y., T. Z. Cao, and X. F. Zhao. 2017. “Applying sensor-based technology to improve construction safety management.” Sensors 17 (8): 1841. https://doi.org/10.3390/s17081841.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 146 • Issue 6 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 12, 2019
Accepted: Sep 24, 2019
Published online: Mar 24, 2020
Published in print: Jun 1, 2020
Discussion open until: Aug 24, 2020
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