To autonomously detect heat loss in building façades using machine learning-aided thermography
Technology & Design
New Frontiers in Research Fund (NFRF)
Description
Efficient inspection of heat loss from building façades is an active research area, driven by the need for improved auditing and monitoring solutions. Thermography is a valuable tool for identifying anomalous heat patterns. This projects presents a novel method for autonomous inspection of heat loss from building façades by combining thermal camera-based imaging, advanced deep learning with YOLOv7 for anomaly detection, and a mathematical model for heat flow quantification. The methodology achieved a mean average precision (mAP@0.5) of 0.770 for anomaly detection when tested on thermal data collected from a multi-unit residential building in an extremely cold climate. The highest heat loss measured at 1533.73 W was from the south façade, while the largest area of heat loss, covering 15.65 m2, was identified on the southeast façade. The project identified 28 regions of thermal anomaly on the building façades, and heat loss from these regions was quantified for inspection.
