## 5 Rebars' Detection Methodology and Results
### Detection Performance Evaluation  
To evaluate the classification performance, the F1-score metric was employed. The F1-score represents the harmonic mean of precision (P) and recall (R), and is particularly effective in scenarios involving imbalanced classes or when both false positives and false negatives have significant implications [37]. It is defined as follows:  
\[F_{1}=2\cdot\frac{P\cdot R}{P+R} \tag{4}\]  
where \(\mathrm{P}~{}=~{}\mathrm{TP}/(\mathrm{TP}+\mathrm{FP})\) is the precision, \(\mathrm{R}~{}=~{}\mathrm{TP}/(\mathrm{TP}+\mathrm{FN})\) is the recall and TP, FP, and FN denote true positives, false positives, and false negatives, respectively. In addition to this quantitative evaluation, a visual assessment of detection quality was performed using graphical representations of the results. Figures 17 and 18 present the outputs obtained for exemplary, chosen thermograms for the case of Experiment Scenario 30 and Experiment Scenario 45, respectively. The top part of each figure shows the thermal image, where vertical bright bands indicate potential reinforcement bars in concrete. Red dashed lines mark the locations identified by the model as containing reinforcement. The lower part of each figure illustrates the probability trajectory, i.e., the output signal of the model across the scanned image. Red 'x' markers denote local maxima of the predicted probability, which are interpreted as likely rebar locations. A strong correlation is observed between the bright vertical patterns in the thermal images and the peaks in the probability curves, confirming the consistency between model predictions and visual features. In Figure 17a, the probability values produced by the model are generally high, with peaks reaching approximately 0.90-0.92. Well-defined probability spikes correspond to the locations of individual reinforcement bars. The valleys between the peaks are also pronounced, indicating the model's strong ability to differentiate between rebar regions and background areas. A total of eight reinforcement bars were identified; nonetheless, slight misalignments between the predicted and actual positions were observed, suggesting that while detection was generally successful, the localization precision was not entirely accurate. In Figure 17b, the reinforcement bars are clearly visible in the thermal image, and the red dashed detection lines closely align with the central axes of the visible bars, suggesting quite accurate localization. The predicted probability values are very high, with several peaks exceeding 0.90 and approaching 0.95. The peaks are sharp and well-separated  
Figure 16: Procedure for detecting reinforcement bars in a thermographic image. from the surrounding valleys, which reflects the model's high confidence and strong signal contrast. In this case, nine bars were detected, which visually appears consistent with the number of observable structures; however, the actual number of bars present is eight, indicating a false-positive detection. In contrast, Figure 17c illustrates a case with lower detection performance. Although reinforcement bars are still partially visible, they exhibit reduced contrast and a more diffuse structure compared to Figure 17a,b. This is especially noticeable in the left and right regions of the image, where the bars are poorly defined. The red detection lines show that only three bars were identified, despite the visual presence of approximately eight to nine bars. This corresponds to a high number of false negatives, i.e., missed detections, resulting in low recall. Although the locations of the three detected bars are accurate, many others were not identified by the model. The maximum probability values in this case are substantially lower, reaching only around 0.79-0.80. The trajectory is noticeably flatter, and the peaks are less distinct. The valleys between adjacent bars are shallow, making it difficult for the peak detection algorithm to reliably distinguish rebar regions from the background.