DSpace Repository

Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study

Show simple item record

dc.contributor.author Barlybayev, Alibek
dc.contributor.author Amangeldy, Nurzada
dc.contributor.author Kurmetbek, Bekbolat
dc.contributor.author Krak, Iurii
dc.contributor.author Razakhova, Bibigul
dc.contributor.author Tursynova, Nazira
dc.contributor.author Turebayeva, Rakhila
dc.date.accessioned 2024-09-17T06:20:56Z
dc.date.available 2024-09-17T06:20:56Z
dc.date.issued 2024
dc.identifier.citation Alibek Barlybayev, Nurzada Amangeldy, Bekbolat Kurmetbek, Iurii Krak, Bibigul Razakhova, Nazira Tursynova & Rakhila Turebayeva (2024) Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study, Cogent Engineering, 11:1, 2333209, DOI: 10.1080/23311916.2024.2333209 ru
dc.identifier.issn 23311916
dc.identifier.other DOI 10.1080/23311916.2024.2333209
dc.identifier.uri http://rep.enu.kz/handle/enu/16444
dc.description.abstract Over the past decade, global industrial and construction growth has underscored the importance of safety. Yet, accidents continue, often with dire outcomes, despite numerous safetyfocused 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. ru
dc.language.iso en ru
dc.publisher Cogent Engineering ru
dc.relation.ispartofseries Том 11, Выпуск 1;Номер статьи 2333209
dc.subject PPE detection system ru
dc.subject YOLOv8 ru
dc.subject image dataset ru
dc.subject construction safety ru
dc.subject object detection ru
dc.subject computer vision ru
dc.title Personal protective equipment detection using YOLOv8 architecture on object detection benchmark datasets: a comparative study ru
dc.type Article ru


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Browse

My Account