Year: 2025
Type: conference-paper
Abstract: Optimising the efficiency of HVAC systems represents a significant opportunity to reduce energy consumption in buildings and mitigate greenhouse gas emissions.This research evaluates low-resolution computer vision algorithms for occupancy detection on resource-constrained embedded systems.Our evaluation focuses specifically on the feasibility of deploying advanced AI object detection models on low-cost hardware platforms (under = C10) with varying computational capabilities.We systematically compared 45 different pre-trained object detection models using the COCO dataset.Among the models evaluated, those with the YOLO backbone proved to be the most suitable for this task.Quantitative analysis showed that YOLOv5n achieved a favourable balance between accuracy (AP50 = 0.944; AP50-95 = 0.584), model size (2.6 MB in RKNN format) and inference time.Performance tests on three embedded platforms -ESP32-CAM (microcontroller), Raspberry Pi Zero 2 W and Luckfox Pico Mini A (single-board computer) -revealed significant differences in inference speed, with hardware-accelerated solutions up to 10,000 times faster than software-only implementations.We have verified real-world applicability using our own ceiling-mounted wide-angle camera dataset.Future work will focus on developing a full hardware prototype, optimising the training dataset with AI-generated synthetic data, and implementing sensor fusion with audio signals for a multimodal approach.
Source: Computer Science Research Notes
DOI: 10.24132/csrn.2025-4
Authors: O. Rudolf, R. Hecker, M. Thißen, L. Sillekens, I. Penner, J.-P. Akelbein, S. Seyfarth, E. Hergenröther
Institution: Darmstadt University of Applied Sciences
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412764700