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

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 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.

Keywords:

• PPE detection system
• YOLOv8
• image dataset
• construction safety
• object detection
• computer vision

Reviewing Editor:

• Jenhui Chen, Chang Gung University, Taiwan

SUBJECT:

• Artificial Intelligence
• Neural Networks
• Image Processing

Disclosure statement

The authors declare that they have no conflicts of interest to report regarding this study.

Additional information

Notes on contributors

Alibek Barlybayev

Alibek Barlybayev received the B.Eng. degree in information systems from L.N. Gumilyov Eurasian National University, Kazakhstan, in 2009 and the M.S. and Ph.D. degrees in computer science from L.N. Gumilyov Eurasian National University, Kazakhstan, in 2011 and 2015, respectively. Currently, he is a Director of the Research Institute of Artificial Intelligence, L.N. Gumilyov Eurasian National University. He is also an Associate Professor of the Department of Artificial Intelligence Technologies, L.N. Gumilyov Eurasian National University, and Higher School of Information Technology and Engineering, Astana International University. His research interests are NLP, the use of neural networks in word processing, smart textbooks, fuzzy logic, stock market price prediction, information security. He can be contacted at email: [email protected]

Nurzada Amangeldy

Nurzada Amangeldy a postdoctoral fellow at L.N. Gumilyov Eurasian National University, specializes in object recognition, computer vision and artificial intelligence. Her leadership in spearheading several key projects, including the creation of the Kazakh sign language recognition system, underscores her innovative approach. Her involvement in the study of detecting personal protective equipment using the YOLOv8 architecture points to her commitment to elevating safety standards through cutting-edge technologies. Set against a backdrop of global trends, Nurzada’s work addresses the pressing need for automated safety solutions, making her contributions timely and relevant. Her research endeavors not only foster technical advancements in the field but also address pressing contemporary challenges.