# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 4 Experimental Results
### Results  
Figure 7 shows the thermal images, segmentation results, and thermal bridge detection results used for validation. The segmentation results are the results of performing GMM-based clustering, thermal anomaly area detection, and DBSCAN-based clustering. In Figure 7, each cluster is displayed in different colors, and the pixels of the same color are in the same cluster. When the thermal images only displayed a wall, as in Cases 3, 6, and 8, the areas corresponding to thermal bridges were segmented well. However, as shown in Cases 1, 2, 4, 5, and 7, if a thermal image contains areas that appear to be thermal defects due to low thermal performance, such as windows and walls, the image contains clusters that are both thermal bridges and not thermal bridges. \begin{table}
\begin{tabular}{c c} \hline \hline
**Title** & **Specification** \\ \hline Measurement range & \(-20\)\({}^{\circ}\)C\(\leftrightarrow\)550 \({}^{\circ}\)C \\ Accuracy & \(\pm 2\)\({}^{\circ}\)C or \(\pm 2\)\% \\ Thermal sensitivity & 0.005 \({}^{\circ}\)C at 30 \({}^{\circ}\)C \\ Wavelength range & 8-14 \(\upmu\)m \\ IR resolution & 320 \(\times\) 240 pixels \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specification of infrared thermal imaging camera used to collect thermal bridge data. Figure 7: _Cont._  
When there are clusters that are thermal bridges and not thermal bridges, clusters corresponding to thermal bridges can be classified by performing machine-learning-based classification after feature extraction. Figure 6(c) shows the thermal bridge detection results for each case. In the detection results, the areas highlighted in green represent the thermal bridge areas detected by the proposed method. As shown in the results, when there is only one thermal bridge, as in Case 1, Case 2, Case 3, Case7, and Case 8, only one cluster was detected as a thermal bridge in the detection results. In Case 6, where there are two thermal bridges, two clusters were detected as thermal bridges. However, in Case 4 and Case 5, the areas where heat was generated because of the window frame and heating pipe were classified as thermal bridges, but they actually were not thermal bridges. This was because these areas had similar shapes as thermal bridges. The results below show that the thermal bridge detection method proposed in this study can automatically detect multiple thermal bridges, even when thermal images contain patterns that appear to be thermal defects other than thermal bridges. Table 2 shows the comparison with the ground truth after applying the proposed method to eight thermal images. The results vary from case to case, but the maximum and minimum precisions were 99.25% and 75.97%, respectively. In terms of cases with low precision, several pixels were recognized as thermal bridges that were not actual thermal bridges, as shown in Cases 4 and 5 in Table 2. This is because the upper frame of the window and the outer wall area adjacent to a hot water pipe, which displayed similar shapes as thermal bridges, were recognized as thermal bridges. The maximum and minimum recalls were 99.08% and 71.46%, respectively. In Case 1, which has a low recall, there are many areas of pixels that are not recognized as thermal bridges, even though they are thermal bridges. This is because they were segmented into different clusters due to temperature differences during the segmentation process. In terms of F-Score, the minimum score was 81.39%, and all the other cases were more than 80%. The average precision, recall, and F-score of the eight thermal images used for validation were 89.29%, 87.29%, and 87.63%, respectively. Although the validation targets are different, the detection results showed improvement, compared to the results of previous studies, as follows: precision, 55%; recall, 68%; and F-score, 61%.