Intelligent Insights into Safety Helmet Usage with Deep Learning

Workplace safety is a critical concern in industrial environments where non-compliance with safety regulations can lead to severe accidents. This project presents an AI-based Industrial Safety Helmet System that leverages deep learning and IoT technologies to ensure compliance with safety protocols and monitor workers' health in real time. The system integrates a camera with AI- based deep learning to detect whether a helmet is being worn. In case of non-compliance, a buzzer is activated, a warning is displayed on the LCD, and an automated email is sent to the concerned authorities for immediate action. To further enhance workplace safety, the system includes a flame sensor to detect potential fire hazards. When fire is detected, the system triggers the buzzer, displays a fire alert on the LCD, and notifies authorities for prompt intervention. Additionally, the system incorporates a MAX30100 sensor to monitor workers' heart rate and SpO2 levels. If any abnormal readings are detected, the system activates the buzzer to alert both the worker and supervisors, enabling early medical intervention. The Arduino microcontroller serves as the central processing unit, efficiently coordinating all system components, including the camera, flame sensor, MAX30100 sensor, LCD display, and buzzer. This proactive AI and IoT-driven safety system significantly reduces workplace risks by ensuring real-time monitoring, rapid hazard detection, and immediate response to emergencies, ultimately enhancing industrial safety standards.