Advanced Face Detection with YOLOv8: Implementation and Integration into AI Modules

This paper presents a comprehensive approach to face detection utilizing the YOLOv8 model, specifically trained on a diverse dataset consisting of images from four individuals. The trained model is seamlessly integrated into an AI module from Huada, a leading AI company, equipped with a camera and LED indicators, enabling real-time face recognition and classification of known and unknown individuals. The model’s performance is evaluated across various metrics, demonstrating its high accuracy, robustness, and efficiency in real-world scenarios. Additionally, the deployment process is detailed, showcasing the practical challenges and solutions encountered during the integration into a security application. Our results indicate that YOLOv8 is not only effective in identifying individuals with high precision but also scalable and adaptable to different environments. This work contributes to the development and deployment of advanced face detection systems, with significant implications for security and surveillance applications.