A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Intelligent Framework for Worker-Machine Safety Assessment
Intelligent safety management based on machine vision has become indispensable in reducing collision safety accidents during construction. To prevent collisions between workers and machines in excavation site construction, a real-time intelligent evaluation system to reflect worker–machine safety status was developed. The system included: (1) determination of the key factors affecting the safety of the interactive operation between workers and machines; (2) extraction of precursor semantic information related to the safety assessment for each object in the construction site based on machine vision; and (3) assessment of the safety state of a monitored object using a fuzzy neural network. A case study of excavation site construction is presented to illustrate and verify the entire process of safety assessment using the developed framework. The results show that the proposed model achieves high detection rates: 96% and 94% for tracking accuracy and 91.67% for prediction accuracy.
Intelligent Framework for Worker-Machine Safety Assessment
Intelligent safety management based on machine vision has become indispensable in reducing collision safety accidents during construction. To prevent collisions between workers and machines in excavation site construction, a real-time intelligent evaluation system to reflect worker–machine safety status was developed. The system included: (1) determination of the key factors affecting the safety of the interactive operation between workers and machines; (2) extraction of precursor semantic information related to the safety assessment for each object in the construction site based on machine vision; and (3) assessment of the safety state of a monitored object using a fuzzy neural network. A case study of excavation site construction is presented to illustrate and verify the entire process of safety assessment using the developed framework. The results show that the proposed model achieves high detection rates: 96% and 94% for tracking accuracy and 91.67% for prediction accuracy.
Intelligent Framework for Worker-Machine Safety Assessment
Hu, Qijun (author) / Bai, Yu (author) / He, Leping (author) / Cai, Qijie (author) / Tang, Shuang (author) / Ma, Guoli (author) / Tan, Jie (author) / Liang, Baowei (author)
2020-03-11
Article (Journal)
Electronic Resource
Unknown
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