Automated Detection of Helmet Wearing with YOLOv8 and Real-Time Monitoring for Factory Safety

Aim/Purpose: The study aimed to develop an automated, real-time monitoring system using YOLOv8 to detect the use of safety helmets in factories, thereby improving workplace safety and compliance. Background: Industrial safety, particularly in factories, is critical for preventing workplace accidents. The detection of safety helmets is an essential component of factory safety protocols; however, manual monitoring can be inefficient and prone to error. This study applies YOLOv8, a state-of-the-art object detection algorithm, to enhance monitoring efficiency by automating the detection process, particularly in challenging conditions such as low-light environments and dynamic settings. Methodology: The YOLOv8 model was trained on a comprehensive dataset of helmet images captured in various industrial settings, encompassing different lighting conditions, angles, and helmet types to ensure the model’s robustness. The system was designed to detect the presence or absence of helmets in real time using video footage. Performance metrics, including precision, recall, and accuracy, were utilized to evaluate the model’s effectiveness, with a focus on addressing the unique challenges of real-world applications. Contribution: This research contributes to the field of industrial safety by offering an automated and scalable solution for real-time helmet detection. The system significantly reduces the need for human intervention in monitoring safety compliance and provides an efficient mechanism for alerting supervisors about safety violations. Findings: The results indicated that the YOLOv8-based detection system achieved high accuracy in real-time helmet detection, even under challenging environmental conditions. The system successfully identified instances of non-compliance, allowing for timely corrective actions. Additionally, the model demonstrated low false-positive and false-negative rates. Statistical significance testing revealed that the model’s performance metrics, including sensitivity and specificity, showed significant improvements compared to previous versions and traditional methods (p < 0.05), underscoring the robustness of YOLOv8 in dynamic factory conditions. Recommendations for Practitioners: Factory managers and safety officers are encouraged to implement this system to enhance safety oversight, reduce the risk of accidents, and streamline the monitoring process without requiring extensive human resources. Recommendation for Researchers: Further research could focus on integrating additional safety features, such as detecting other protective gear (e.g., gloves, goggles), and expanding the dataset to include more diverse industrial environments. Impact on Society: Enhancing safety protocols in industrial settings has the potential to significantly reduce workplace injuries, leading to safer working environments and greater compliance with safety regulations. Future Research: Future research may explore the integration of this system with Internet of Things (IoT) devices for broader safety monitoring and the application of YOLOv8 in detecting other safety hazards beyond helmet use.