Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks
Many industrial accidents occur at construction sites. Several countries are instating safety management measures to reduce industrial accidents at construction sites. However, there are few technical measures relevant to this task, and there are safety blind spots related to differences in human re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/2/694 |
| _version_ | 1797496131976953856 |
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| author | Jeongeun Park Hyunjae Lee Ha Young Kim |
| author_facet | Jeongeun Park Hyunjae Lee Ha Young Kim |
| author_sort | Jeongeun Park |
| collection | DOAJ |
| description | Many industrial accidents occur at construction sites. Several countries are instating safety management measures to reduce industrial accidents at construction sites. However, there are few technical measures relevant to this task, and there are safety blind spots related to differences in human resources’ capabilities. We propose a deep convolutional neural network that automatically recognizes possible material and human risk factors in the field regardless of individual management capabilities. The most suitable learning method and model for this study’s task and environment were experimentally identified, and visualization was performed to increase the interpretability of the model’s prediction results. The fine-tuned Safety-MobileNet model showed a high performance of 99.79% (30 ms), demonstrating its high potential to be applied in actual construction sites. In addition, via visualization, the cause of the model’s confusion of classes could be found in a dataset that the model did not predict correctly, and insights for result analysis could be presented. The material and human risk factor recognition model presented in this study can contribute to solving various practical problems, such as the absence of accident prevention systems, the limitations of human resources for safety management, and the difficulties in applying safety management systems to small construction companies. |
| first_indexed | 2024-03-10T01:59:17Z |
| format | Article |
| id | doaj.art-d972afeaba4643269b9ee29abb9e90be |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:59:17Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d972afeaba4643269b9ee29abb9e90be2023-11-23T12:51:15ZengMDPI AGApplied Sciences2076-34172022-01-0112269410.3390/app12020694Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural NetworksJeongeun Park0Hyunjae Lee1Ha Young Kim2Graduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, KoreaGraduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, KoreaGraduate School of Information, Yonsei University, Yonsei-ro 50, Seodaemun-gu, Seoul 03722, KoreaMany industrial accidents occur at construction sites. Several countries are instating safety management measures to reduce industrial accidents at construction sites. However, there are few technical measures relevant to this task, and there are safety blind spots related to differences in human resources’ capabilities. We propose a deep convolutional neural network that automatically recognizes possible material and human risk factors in the field regardless of individual management capabilities. The most suitable learning method and model for this study’s task and environment were experimentally identified, and visualization was performed to increase the interpretability of the model’s prediction results. The fine-tuned Safety-MobileNet model showed a high performance of 99.79% (30 ms), demonstrating its high potential to be applied in actual construction sites. In addition, via visualization, the cause of the model’s confusion of classes could be found in a dataset that the model did not predict correctly, and insights for result analysis could be presented. The material and human risk factor recognition model presented in this study can contribute to solving various practical problems, such as the absence of accident prevention systems, the limitations of human resources for safety management, and the difficulties in applying safety management systems to small construction companies.https://www.mdpi.com/2076-3417/12/2/694construction siterisk factorssafety managementdeep learningconvolutional neural networkvisualization |
| spellingShingle | Jeongeun Park Hyunjae Lee Ha Young Kim Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks Applied Sciences construction site risk factors safety management deep learning convolutional neural network visualization |
| title | Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks |
| title_full | Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks |
| title_fullStr | Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks |
| title_full_unstemmed | Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks |
| title_short | Risk Factor Recognition for Automatic Safety Management in Construction Sites Using Fast Deep Convolutional Neural Networks |
| title_sort | risk factor recognition for automatic safety management in construction sites using fast deep convolutional neural networks |
| topic | construction site risk factors safety management deep learning convolutional neural network visualization |
| url | https://www.mdpi.com/2076-3417/12/2/694 |
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