用低分辨率热成像实现隐私保护的人员占用检测
A computer vision-based model for occupancy detection using low-resolution thermal images
- 基于YOLOv5和迁移学习，利用低分辨率热图识别人员
- 精度、召回率、mAP50均接近1.000，表现优异
- 兼顾隐私安全与计算效率，适合智能楼宇应用
人员占用状态对暖通空调（HVAC）系统的能耗和运行有重要影响。传统HVAC采用固定时序运行，未考虑实际占用情况。先进的人体中心控制（OCC）通过感知占用状态来调节HVAC运行。虽然结合计算机视觉（CV）技术的RGB图像被广泛用于占用检测，但其捕捉的面部和身体细节引发严重隐私问题。低分辨率热成像提供了一种非侵入式解决方案，有效缓解隐私担忧。本研究开发了一种基于低分辨率热成像与计算机视觉技术的占用检测模型，采用迁移学习微调了YOLOv5模型。最终模型在各项指标上表现良好，精度、召回率、mAP50均接近1.000。该模型不仅缓解了隐私问题，还降低了计算资源需求。
原文摘要 · Abstract (English)
Occupancy plays an essential role in influencing the energy consumption and operation of heating, ventilation, and air conditioning (HVAC) systems. Traditional HVAC typically operate on fixed schedules without considering occupancy. Advanced occupant-centric control (OCC) adopted occupancy status in regulating HVAC operations. RGB images combined with computer vision (CV) techniques are widely used for occupancy detection, however, the detailed facial and body features they capture raise significant privacy concerns. Low-resolution thermal images offer a non-invasive solution that mitigates privacy issues. The study developed an occupancy detection model utilizing low-resolution thermal images and CV techniques, where transfer learning was applied to fine-tune the You Only Look Once version 5 (YOLOv5) model. The developed model ultimately achieved satisfactory performance, with precision, recall, mAP50, and mAP50 values approaching 1.000. The contributions of this model lie not only in mitigating privacy concerns but also in reducing computing resource demands.
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