Enhancing safety in industry 5.0: human-computer collaboration benefits through a dataset of protective equipment detection

This paper presents the development and implementation of a comprehensive dataset designed to enhance workplace safety in industrial environments through advanced computer vision technologies. The dataset focuses on the detection of essential protective equipment, such as helmets and vests, worn by workers in various industrial settings. Utilizing this dataset, a YOLOv8-based computer vision model is trained to achieve 82.1% mAP accuracy (70.5% for helmets, 93.7% for vests) in real-time identification of whether workers are equipped with the appropriate safety gear, demonstrating high reliability for safety compliance monitoring. This initiative is part of the European Research Project TALON, which aims to demonstrate the potential of collaborative efforts between humans and machines in achieving higher safety standards, through its 4th pilot. TALON system will enable automated, flexible, adaptable, programmable, explainable and energy-efficient edge Artificial Intelligence (AI) networking by developing complementary technologies such as AI orchestrator, blockchain, edge networking and digital twins (DTs) in an integrated and innovative way. The dataset, along with the developed predictive model, offers a significant contribution to the field of safety, showcasing how technological advancements can be leveraged to safeguard human lives in the workplace.