Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network

Abstract

Detecting thermal bridges in building envelopes should be a priority to improve the thermal performance of buildings. Recently, thermographic surveys are being used to detect thermal bridges. However, conventional methods of detecting thermal bridges from thermal images rely on the subjective judgment of audits. Research has been conducted to automatically detect thermal bridges from thermal images to improve problems caused by such subjective judgment, but most of these studies are still in the early stage. Therefore, this study proposes a linear thermal bridge detection method based on image processing and machine learning. The proposed method includes thermal anomaly area clustering, feature extraction, and an artificial-neural-network-based thermal bridge detection. The proposed method was validated by detecting the thermal bridges in actual buildings. As a result, the average precision, recall, and F-score were 89.29%, 87.29, and 87.63%, respectively.

Evaluation of Preferences for a Thermal-Camera-Based Abnormal Situation Detection Service via the Integrated Fuzzy AHP/TOPSIS Model

“…This was particularly understandable by their preference for the detection service of the pre-fire phenomenon. This is consistent with previous studies of such areas as fire propagation, volcanic monitoring, and building thermal bridge diagnosis [22,[40][41][42][43][44]46,47]. Meanwhile, local governments highly evaluated the controlling situations related to citizens' safety within the thermography camera service area.…”

“…This indicates diverse thermography-based image analysis studies for preventing safety accidents in road and transportation. Some studies have also diagnosed buildings using thermal cameras, including detection of issues in building exteriors (e.g., corrosion of steel bars in concrete structures of buildings) [38,39] and diagnosis of thermal bridges in buildings [40][41][42][43][44]. In relation to HW, one study analyzed the effects of the performance and variability of thermal cameras on changes in the body surface temperature of horses [45].…”

Protocolo para el uso de vehículos aéreos no tripulados en la inspección de edificios agroindustriales

“…However, it is important to remark that thermal images interpretation involves a high level of subjectivity and mainly relies on the expertise of the human operator, as pointed out by Garrido et al (23). For that reason, some research has aimed at developing methods for automatic detection and characterisation of thermal bridges (23,24).…”

Thermal Bridges on Building Rooftops

“…Currently, approaches for automated thermal bridge detection work mostly with temperature threshold values and pattern recognition 13 – 16 . It is, however, difficult to find threshold values that can be generally applied to all types of thermal bridges 17 . Patterns and temperatures differ depending on the materials and building components where thermal bridges occur, on environmental conditions, and on recording settings.…”