9791221502893_62.pdf

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...

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Γλώσσα:English
Έκδοση: Firenze University Press 2024
Διαθέσιμο Online:https://books.fupress.com/doi/capitoli/979-12-215-0289-3_62
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spelling oapen-20.500.12657-890702024-04-03T02:23:09Z Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker Lee, Seungsoo Choi, Woonggyu Park, Minsoo Jeon, Yuntae Quoc Tran, Dai Park, Seunghee deep learning keypoint detection pose estimation computer vision construction site safe thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications::UTV Virtualization 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 2024-04-02T15:45:36Z 2024-04-02T15:45:36Z 2023 chapter ONIX_20240402_9791221502893_39 2704-5846 9791221502893 https://library.oapen.org/handle/20.500.12657/89070 eng Proceedings e report application/pdf n/a 9791221502893_62.pdf https://books.fupress.com/doi/capitoli/979-12-215-0289-3_62 Firenze University Press 10.36253/979-12-215-0289-3.62 10.36253/979-12-215-0289-3.62 bf65d21a-78e5-4ba2-983a-dbfa90962870 9791221502893 137 7 Florence open access
institution OAPEN
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language English
description 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
title 9791221502893_62.pdf
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title_full 9791221502893_62.pdf
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title_full_unstemmed 9791221502893_62.pdf
title_sort 9791221502893_62.pdf
publisher Firenze University Press
publishDate 2024
url https://books.fupress.com/doi/capitoli/979-12-215-0289-3_62
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