IOT-Enabled Smart Home Automation with YOLOv5 Human Detection

This paper demonstrates an IoT connected Smart Home automation using Realtime Human Detection and Personalized Environment Adaptation based on YOLOv5 model. In this study, we proposed a deep learning and IoT integration system that would provide convenience, privacy, and energy efficiency in homes of today. The device controls lighting and HVAC systems in real time by detecting human presence, thus saving with energy and making user feel comfort. Experiments show that it can reach 95% accuracy in detection on any possible real-world data, respond in real-time with low latency and save a non-trivial amount of energy. In addition, the system achieves this by drastically lowering data transmission to the outside and keeping most of it local. This model scales very well with performance in multi-user environments verified by scalability tests. The present work underscores the transformative power of AI-enabled IoT systems in the architectural automation of intelligent homes with user-driven solutions.