Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques
Traumatic brain injuries (from falls and electrocution), sprains, broken bones, and other injuries can result from slipping and falling on the ground, leaking gas that is hazardous to inhale and collisions are the primary causes of construction fatalities (resulting from being struck by objects). Th...
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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39912/ http://umpir.ump.edu.my/id/eprint/39912/1/EA18167_Hadif_Thesis%20-%20Hadif.pdf |
| _version_ | 1848825900094717952 |
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| author | Muhammad Hadif, Dzulkhissham |
| author_facet | Muhammad Hadif, Dzulkhissham |
| author_sort | Muhammad Hadif, Dzulkhissham |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Traumatic brain injuries (from falls and electrocution), sprains, broken bones, and other injuries can result from slipping and falling on the ground, leaking gas that is hazardous to inhale and collisions are the primary causes of construction fatalities (resulting from being struck by objects). The Department of Occupational Safety and Health (DOSH) in Malaysia mandates contractors to always enforce and monitor adequate Personal Protective Equipment (PPE) for workers (e.g., hard helmet and vest) as a preventative measure. In addition, because of the COVID-19 outbreak over the last two years, wearing a face mask in factories, departments, and working offices is critical. This paper presents a deep learning technique for detecting multiple personal protection equipment at once based on You-Only-Look-Once Version 4 (YOLOv4) object detection algorithm. The whole training process or computation is done in Google Colaboratory. The training result shows that the Mean Average Precision (mAP) for the best weight training is up to 97.04% for detecting multiple PPE by using this method. |
| first_indexed | 2025-11-15T03:36:16Z |
| format | Undergraduates Project Papers |
| id | ump-39912 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:36:16Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-399122024-01-08T10:21:19Z http://umpir.ump.edu.my/id/eprint/39912/ Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques Muhammad Hadif, Dzulkhissham TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Traumatic brain injuries (from falls and electrocution), sprains, broken bones, and other injuries can result from slipping and falling on the ground, leaking gas that is hazardous to inhale and collisions are the primary causes of construction fatalities (resulting from being struck by objects). The Department of Occupational Safety and Health (DOSH) in Malaysia mandates contractors to always enforce and monitor adequate Personal Protective Equipment (PPE) for workers (e.g., hard helmet and vest) as a preventative measure. In addition, because of the COVID-19 outbreak over the last two years, wearing a face mask in factories, departments, and working offices is critical. This paper presents a deep learning technique for detecting multiple personal protection equipment at once based on You-Only-Look-Once Version 4 (YOLOv4) object detection algorithm. The whole training process or computation is done in Google Colaboratory. The training result shows that the Mean Average Precision (mAP) for the best weight training is up to 97.04% for detecting multiple PPE by using this method. 2022-06 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39912/1/EA18167_Hadif_Thesis%20-%20Hadif.pdf Muhammad Hadif, Dzulkhissham (2022) Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah. |
| spellingShingle | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Muhammad Hadif, Dzulkhissham Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques |
| title | Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques |
| title_full | Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques |
| title_fullStr | Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques |
| title_full_unstemmed | Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques |
| title_short | Real-Time Detection of Personal Protective Equipment for Site Safety Using Deep Learning Techniques |
| title_sort | real-time detection of personal protective equipment for site safety using deep learning techniques |
| topic | TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/39912/ http://umpir.ump.edu.my/id/eprint/39912/1/EA18167_Hadif_Thesis%20-%20Hadif.pdf |