Deep Learning-Based Face and Helmet Detection System for Workplace Safety and Attendance Tracking

In modern industrial and construction environments, maintaining accurate employee attendance and enforcing strict safety compliance are vital components of efficient workforce management.This project presents an AI-driven, real-time attendance and safety monitoring system that leverages deep learning technologies to address these critical needs.The system integrates facial recognition using a high-precision deep learning model from the face recognition library and helmet detection using the YOLOv8 object detection algorithm.It ensures that attendance is marked only when an employee is both properly identified and wearing the required safety helmet, thereby promoting safety standards while eliminating identity fraud or proxy attendance.Captured data such as employee name, helmet status, and timestamp is stored securely in Firebase Firestore, providing real-time synchronization and robust cloud-based data management.An intelligent alert mechanism is also embedded into the system, which triggers a notification when unauthorized individuals or non-compliant workers are detected, enhancing on-site security and proactive incident response.A Flutterbased mobile application complements the system by providing real-time access to attendance and safety compliance records, offering a user-friendly interface for administrators and supervisors to monitor workforce activities.This intelligent framework not only automates routine attendance tasks but also supports scalable safety enforcement, contributing to a safer and more accountable working environment.By combining artificial intelligence, cloud computing, and real-time monitoring, the system paves the way for smarter workforce governance in safety-critical sectors.