Occupancy Detection for HVAC Systems Using IoT Edge Computing and Vision-Based Image Processing

Energy efficiency, particularly in Heating, Ventilation, and Air Conditioning (HVAC) systems, is a critical challenge in modern building management due to the increasing energy demands and environmental impacts. This paper focuses on developing optimized object detection models using machine vision for occupancy detection in office environments, aiming to improve HVAC efficiency. The primary objective is to compare three models—YOLOv8n, YOLOv9c, and YOLOv10n—against the Faster R-CNN baseline, emphasizing detection speed, computational efficiency, and small object detection. Data collection involved creating a custom dataset of 1,728 images from office environments, annotated with eight object classes, including persons and office devices. Preprocessing techniques such as grayscale conversion, image resizing, and augmentation improved the model’s ability to detect objects under various conditions, including occlusion and varied camera angles. The models were evaluated based on mAP@50, mAP@50-95, and detection speed. YOLOv9c outperformed Faster R-CNN in speed and accuracy, achieving a mAP@50 of 88.0% and mAP@50-95 of 59.8%, making it the most balanced model. YOLOv8n demonstrated the fastest detection speed, ideal for real-time applications, while YOLOv10n, though less accurate, provided a strong trade-off between speed and precision. Despite these successes, challenges remain, particularly in small object detection and dataset size. Future work includes expanding the dataset to 100,000 images, improving detection of smaller objects, and integrating the object detection models into real-time HVAC control systems. Moreover, deployment on edge devices, transfer learning, and integration with Building Management Systems (BMS) for dynamic HVAC control represent promising areas for future research.