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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/944 |
| _version_ | 1797437257486958592 |
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| author | Jaekyu Lee Sangyub Lee |
| author_facet | Jaekyu Lee Sangyub Lee |
| author_sort | Jaekyu Lee |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T11:16:15Z |
| format | Article |
| id | doaj.art-c30c0eca056a4205ae9f920432734d3a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:16:15Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c30c0eca056a4205ae9f920432734d3a2023-12-01T00:30:14ZengMDPI AGSensors1424-82202023-01-0123294410.3390/s23020944Construction Site Safety Management: A Computer Vision and Deep Learning ApproachJaekyu Lee0Sangyub Lee1Energy IT Convergence Research Center, Korea Electronics Technology Institute, Seongnam-si 13509, Republic of KoreaEnergy IT Convergence Research Center, Korea Electronics Technology Institute, Seongnam-si 13509, Republic of KoreaIn 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.https://www.mdpi.com/1424-8220/23/2/944worker safety managementvirtual datasetssynthetic datasetsimage processingtransfer learningvirtual validation environment |
| spellingShingle | Jaekyu Lee Sangyub Lee Construction Site Safety Management: A Computer Vision and Deep Learning Approach Sensors worker safety management virtual datasets synthetic datasets image processing transfer learning virtual validation environment |
| 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 | worker safety management virtual datasets synthetic datasets image processing transfer learning virtual validation environment |
| url | https://www.mdpi.com/1424-8220/23/2/944 |
| work_keys_str_mv | AT jaekyulee constructionsitesafetymanagementacomputervisionanddeeplearningapproach AT sangyublee constructionsitesafetymanagementacomputervisionanddeeplearningapproach |