Real-Time Detection System for PPE Usage Using Computer Vision

Resumo

According to data from eSocial, part of the Ministry of Labor and Employment, nearly 500,000 work-related accidents were recorded in Brazil in 2023, 2,888 of which were fatal. A survey by the Occupational Health and Safety Observatory, covering data from 2012 to 2022, revealed that the country registered more than 7 million work accidents during that period, considering only formally employed workers under the CLT regime. Currently, Brazil ranks 4th in the global ranking of occupational accidents, behind only China, India, and Indonesia. These figures highlight the importance of occupational safety, especially in industrial and construction environments, where the proper use of Personal Protective Equipment (PPE), such as safety helmets, is essential to prevent accidents and protect workers' physical integrity. However, PPE compliance is still predominantly monitored manually, which poses significant limitations, particularly in areas with high worker traffic. In light of this scenario, this project proposes the development of an intelligent real-time monitoring system capable of automatically detecting the absence of safety helmets using computer vision techniques applied to images captured by surveillance cameras. To achieve this, the YOLO (You Only Look Once) convolutional neural network was employed, a model widely recognized for its high accuracy and efficiency in real-time object detection tasks. The developed system identifies workers not wearing helmets, captures images of the infraction, and automatically sends alerts to the company’s monitoring center. The methodology involved image collection, model training using YOLO, system integration development, and testing in simulated environments. The results showed an accuracy of over 90% in detecting the absence of helmets, demonstrating the effectiveness of the proposed solution. In addition to automating compliance monitoring, the system reduces reliance on human supervision, strengthens adherence to regulatory standards, and promotes safer workplaces. The proposal also stands out for its accessibility, robustness, and practical applicability, offering a promising alternative for modernizing occupational safety management.