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Construction Site Safety Management: A Computer Vision and Deep Learning Approach

In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first obj...

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Autores principales: Lee, Jaekyu, Lee, Sangyub
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863726/
https://www.ncbi.nlm.nih.gov/pubmed/36679738
http://dx.doi.org/10.3390/s23020944
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author Lee, Jaekyu
Lee, Sangyub
author_facet Lee, Jaekyu
Lee, Sangyub
author_sort Lee, Jaekyu
collection PubMed
description In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning.
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spelling pubmed-98637262023-01-22 Construction Site Safety Management: A Computer Vision and Deep Learning Approach Lee, Jaekyu Lee, Sangyub Sensors (Basel) Article In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning. MDPI 2023-01-13 /pmc/articles/PMC9863726/ /pubmed/36679738 http://dx.doi.org/10.3390/s23020944 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lee, Jaekyu
Lee, Sangyub
Construction Site Safety Management: A Computer Vision and Deep Learning Approach
title Construction Site Safety Management: A Computer Vision and Deep Learning Approach
title_full Construction Site Safety Management: A Computer Vision and Deep Learning Approach
title_fullStr Construction Site Safety Management: A Computer Vision and Deep Learning Approach
title_full_unstemmed Construction Site Safety Management: A Computer Vision and Deep Learning Approach
title_short Construction Site Safety Management: A Computer Vision and Deep Learning Approach
title_sort construction site safety management: a computer vision and deep learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863726/
https://www.ncbi.nlm.nih.gov/pubmed/36679738
http://dx.doi.org/10.3390/s23020944
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