Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study
AbstractOver the past decade, global industrial and construction growth has underscored the importance of safety. Yet, accidents continue, often with dire outcomes, despite numerous safety-focused initiatives. Addressing this, this article introduces a novel approach using YOLOv8, a rapid object det...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2024.2333209 |
| _version_ | 1797213030583369728 |
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| author | Alibek Barlybayev Nurzada Amangeldy Bekbolat Kurmetbek Iurii Krak Bibigul Razakhova Nazira Tursynova Rakhila Turebayeva |
| author_facet | Alibek Barlybayev Nurzada Amangeldy Bekbolat Kurmetbek Iurii Krak Bibigul Razakhova Nazira Tursynova Rakhila Turebayeva |
| author_sort | Alibek Barlybayev |
| collection | DOAJ |
| description | AbstractOver the past decade, global industrial and construction growth has underscored the importance of safety. Yet, accidents continue, often with dire outcomes, despite numerous safety-focused initiatives. Addressing this, this article introduces a novel approach using YOLOv8, a rapid object detection model, for recognizing personal protective equipment (PPE). This method, leveraging computer vision (CV) instead of traditional sensor-based systems, offers an economical, simpler and field-friendly solution. We established the Color Helmet and Vest (CHV) and Safety HELmet dataset with 5K images (SHEL5K) datasets, comprising eight object classes like helmets, vests and goggles, to detect worker-worn PPE. After categorizing the dataset into training, testing and validation subsets, diverse YOLOv8 models were assessed based on metrics including precision, recall and mAP50. Notably, YOLOv8x and YOLOv8l excelled in PPE detection, particularly in recognizing person and vest categories. This innovative CV-driven method promises real-time PPE detection, fortifying worker safety on construction sites. |
| first_indexed | 2024-04-24T10:51:48Z |
| format | Article |
| id | doaj.art-1c86e5130a41481ea196ce0f57313841 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-04-24T10:51:48Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-1c86e5130a41481ea196ce0f573138412024-04-12T08:01:49ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2024.2333209Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative studyAlibek Barlybayev0Nurzada Amangeldy1Bekbolat Kurmetbek2Iurii Krak3Bibigul Razakhova4Nazira Tursynova5Rakhila Turebayeva6Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, KazakhstanFaculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, KazakhstanFaculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, KazakhstanFaculty of Computer Science and Cybernetics, Taras Shevchenko National University of Kyiv, Kyiv, UkraineFaculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, KazakhstanFaculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, KazakhstanFaculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, KazakhstanAbstractOver the past decade, global industrial and construction growth has underscored the importance of safety. Yet, accidents continue, often with dire outcomes, despite numerous safety-focused initiatives. Addressing this, this article introduces a novel approach using YOLOv8, a rapid object detection model, for recognizing personal protective equipment (PPE). This method, leveraging computer vision (CV) instead of traditional sensor-based systems, offers an economical, simpler and field-friendly solution. We established the Color Helmet and Vest (CHV) and Safety HELmet dataset with 5K images (SHEL5K) datasets, comprising eight object classes like helmets, vests and goggles, to detect worker-worn PPE. After categorizing the dataset into training, testing and validation subsets, diverse YOLOv8 models were assessed based on metrics including precision, recall and mAP50. Notably, YOLOv8x and YOLOv8l excelled in PPE detection, particularly in recognizing person and vest categories. This innovative CV-driven method promises real-time PPE detection, fortifying worker safety on construction sites.https://www.tandfonline.com/doi/10.1080/23311916.2024.2333209PPE detection systemYOLOv8image datasetconstruction safetyobject detectioncomputer vision |
| spellingShingle | Alibek Barlybayev Nurzada Amangeldy Bekbolat Kurmetbek Iurii Krak Bibigul Razakhova Nazira Tursynova Rakhila Turebayeva Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study Cogent Engineering PPE detection system YOLOv8 image dataset construction safety object detection computer vision |
| title | Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study |
| title_full | Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study |
| title_fullStr | Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study |
| title_full_unstemmed | Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study |
| title_short | Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study |
| title_sort | personal protective equipment detection using yolov8 architecture on object detection benchmark datasets a comparative study |
| topic | PPE detection system YOLOv8 image dataset construction safety object detection computer vision |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2024.2333209 |
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