Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker Minsoo Park Sungkyunkwan University, Korea, Republic of Seungsoo Lee Sungkyunkwan University, Korea, Republic of Woonggyu Choi Sungkyunkwan University, Korea, Republic of Yuntae Jeon Sungkyunkwan University, Korea, Republic of Dai Quoc Tran Sungkyunkwan University, Korea, Republic of Seunghee Park Sungkyunkwan University, Korea, Republic of This is a section of CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality (DOI: 10.36253/979-12-215-0289-3) by Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi Firenze University Press Florence 2023 https://doi.org/10.36253/10.36253/979-12-215-0289-3.62

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Fall from height (FFH) is one of the major causes of injury and fatalities in construction industry. Deep learning-based computer vision for safety monitoring has gained attention due to its relatively lower initial cost compared to traditional sensing technologies. However, a single detection model that has been used in many related studies cannot consider various contexts at the construction site. In this paper, we propose a deep learning-based pose estimation approach for identifying potential fall hazards of construction workers. This approach can relatively increase the accuracy of estimating the distance between the worker and the fall hazard area compared to the existing methods from the experimental results. Our proposed approach can improve the robustness of worker location estimation compared to existing methods in complex construction site environments with obstacles that can obstruct the worker's position. Also, it is possible to provide information on whether a worker is aware of a potential fall risk area. Our approach can contribute to preventing FFH by providing access information to fall risk areas such as construction site openings and inducing workers to recognize the risk area even in Inattentional blindness (IB) situations

 deep learning keypoint detection pose estimation computer vision construction site safe

It is available online at https://doi.org/10.36253/10.36253/979-12-215-0289-3.62

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