Industrial Safety Management Using Deep Learning

Abstract

Industrial safety management is crucial in safeguarding workplaces and preventing accidents in industrial environments. Integrating deep learning methods has emerged as a promising approach to enhance safety protocols by leveraging advanced algorithms for rapid hazard identification and mitigation. This paper aims to present strategies for addressing current challenges in industrial safety, including concerns related to worker well-being and machinery operation. A comprehensive review highlights the limitations of existing safety systems, underscoring the need for more effective solutions. The proposed project seeks to bridge this gap by developing a real-time deep-learning system for detecting unsafe conditions. Key objectives include identifying workers without proper safety attire, detecting falls, and dynamically monitoring worker presence in hazardous areas. Additionally, the paper explores the potential of computer vision techniques in enhancing industrial safety measures. This initiative aims to advance industrial safety practices and ensure secure work environments for all stakeholders by providing a detailed examination of these technologies.