Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker
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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| Format: | Online |
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Firenze University Press
2024
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| Online dostop: | ONIX_20240402_9791221502893_39 |
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| _version_ | 1869514235042594816 |
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| author | Lee, Seungsoo Choi, Woonggyu Park, Minsoo Jeon, Yuntae Quoc Tran, Dai Park, Seunghee |
| author_browse | Choi, Woonggyu Jeon, Yuntae Lee, Seungsoo Park, Minsoo Park, Seunghee Quoc Tran, Dai |
| author_facet | Lee, Seungsoo Choi, Woonggyu Park, Minsoo Jeon, Yuntae Quoc Tran, Dai Park, Seunghee |
| author_sort | Lee, Seungsoo |
| collection | Directory of Open Access Books |
| 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 |
| format | Online |
| id | doab-20.500.12854ir-137101 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1371012024-05-12T04:26:16Z 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 2024-05-12T04:26:12Z 2024-05-12T04:26:12Z 2024-04-02T15:45:36Z 2023 chapter ONIX_20240402_9791221502893_39 2704-5846 https://library.oapen.org/handle/20.500.12657/89070 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137101 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89070/1/9791221502893_62.pdf Firenze University Press 10.36253/979-12-215-0289-3.62 10.36253/979-12-215-0289-3.62 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 7 Florence open access |
| spellingShingle | 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 Lee, Seungsoo Choi, Woonggyu Park, Minsoo Jeon, Yuntae Quoc Tran, Dai Park, Seunghee Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker |
| title | Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker |
| title_full | Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker |
| title_fullStr | Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker |
| title_full_unstemmed | Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker |
| title_short | Chapter Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker |
| title_sort | chapter deep learning based pose estimation for identifying potential fall hazards of construction worker |
| topic | 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 |
| topic_facet | 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 |
| url | ONIX_20240402_9791221502893_39 |
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