## 5 Rebars' Detection Methodology and Results
### Detection Performance Evaluation
In the right portion of the image, the probabilities decrease to approximately 0.50 or lower, suggesting the lack of a detectable thermal response from the reinforcement in those regions. This scenario corresponds to a case with a low F1-score. The reduced performance in this instance may result from the poor quality of the input thermal data. The thermal image may be noisier, with weaker contrast between the bars and the surrounding concrete. Additionally, in Experiment Scenario 30, the bars are not perfectly aligned, which may hinder the effectiveness of the fixed-size sliding window, making it more difficult to accurately capture features of interest. Furthermore, the presence of structural disturbances, such as cracks or surface irregularities, may disrupt the thermal pattern and interfere with the detection process. For the case of Experiment Scenario 45, where the overall detection performance was superior, Figure 18a illustrates a situation in which vertical thermal patterns corresponding to reinforcement bars are clearly visible. The red dashed detection lines are largely consistent with the positions of these patterns. One centrally located bar appears somewhat blurred, yet it was successfully detected. The predicted probability values are high, with peaks exceeding 0.90 in several regions. These distinct peaks are well-aligned with individual bars, and the valleys between them are well-defined, indicating the model's effective ability to distinguish bar regions from surrounding concrete. A total of eight bars were detected, which corresponds exactly to the number of reinforcement elements that are visually observable in the thermal image. This instance represents a high-performance detection case, yielding an F1-score of 1.0. Under the conditions of Experiment Scenario 45, the model demonstrated excellent predictive capabilities. Figure 18b, also from Experiment Scenario 45, shows similarly strong performance. The reinforcement bars are sharply defined, and the detection lines align accurately with the bar centers. The model predicts consistently high probabilities, with numerous peaks above 0.90, some approaching 0.95. These peaks are sharp and clearly separated, reflecting the model's high confidence and robustness in localizing the bars. The deep valleys between peaks confirm strong signal contrast. Although nine bars were detected visually, the actual number of embedded rebars was eight, indicating one false positive. Nevertheless, the detection achieves a very high F1-score of 0.9412, suggesting that Experiment Scenario 45 offers optimal imaging conditions for the applied methodology. In contrast, Figure 18c--also part of Experiment Scenario 45--demonstrates reduced detection accuracy. Although reinforcement bars remain partially visible, they are less distinct and more diffuse, particularly in the right-hand section of the image. Only three bars were detected, while visual inspection reveals many more (approximately eight to nine), implying a high rate of false negatives and thus low recall. The maximum predicted probabilities are considerably lower, reaching only around 0.79-0.80. The signal trajectory is flattened, with less pronounced peaks, and the valleys are shallow, complicating reliable peak discrimination. In the rightmost region, probability values drop to around 0.40, indicating a very weak thermal response. This case is associated with a low-performance outcome, reflected by an F1-score of 0.5455, and underscores the limitations of the detection approach when exposed to noisy thermal data, suboptimal contrast, or structural disturbances. The diminished contrast may stem from poorer thermal image quality, blurred bar outlines, or non-uniform bar alignment, all of which negatively affect the effectiveness of the fixed-size sliding window approach used in this study. Figure 19 presents a visualization of the distribution of F1-score values for the two experimental configurations: Experiment Scenario 30 and Experiment Scenario 45.