# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 5 Conclusions  
This study proposed a new method for detecting linear thermal bridges from thermal images, using image processing and ANN. After performing GMM-based clustering, using temperature, the average temperature of each cluster was calculated, to compare with normal walls. Based on winter measurements, areas with higher temperatures than normal walls were classified as thermal anomaly areas, and DBSCAN was applied to divide the thermal anomaly areas into individual defects, to perform density-based clustering. Subsequently, feature values reflecting morphological characteristics of thermal bridges were extracted from each cluster. Based on the extracted feature values, only thermal bridges were classified by using a trained model, using ANN. A field experiment was performed to validate the thermal bridge detection method proposed in this study. Thermal images were taken of buildings with actual thermal bridges, and the precision, recall, and F-score were calculated. As a result, the average precision, recall, and F-score were 89.29%, 87.29%, and 87.63%, respectively.  
The contributions of this study are as follows. First, it proposed a feature extraction method that reflects the morphological characteristics of thermal bridges. The proposed features can be used in other fields that require feature extraction of objects with linear or flat shapes, in addition to thermal bridges. Second, this study proposed a thermal bridge detection process using machine learning. Finally, from a practical aspect, the method proposed in this study secures objectivity by automatically detecting thermal bridges, which relied on subjective judgment in conventional audits. This study proposed a method for detecting only thermal bridges among various thermal defects in building envelopes. Further research will be conducted to propose a method to detect defects other than thermal bridges.  
Conceptualization, C.K. and H.J.; methodology, C.K. and E.-J.K.; software, C.K.; validation, C.K. and J.-S.C.; formal analysis, C.K. and J.-S.C.; investigation, J.-S.C.; resources, C.K. and J.-S.C.; data curation, C.K. and J.-S.C.; writing--original draft preparation, C.K.; writing--review and editing, E.-J.K. and H.J.; visualization, C.K. and J.-S.C.; supervision, E.-J.K. and H.J.; project administration, E.-J.K. and H.J.; funding acquisition, C.K. and H.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2018R1D1A1B07041890). This work was supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 20 CTAP-C152248-02).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.