## 5 Rebars' Detection Methodology and Results
### Binary Classification Model  
In this study, the ResNet-18 convolutional neural network was employed to analyze thermographic images obtained through active microwave thermography [36]. ResNet-18, a member of the Residual Network (ResNet) family, introduces a key architectural innovation known as residual connections. These connections help mitigate the vanishing gradient problem that often occurs in deep neural networks, particularly during the training of very deep architectures. Instead of learning direct mapping from input to output, the network learns the residual--that is, the difference between the input and the desired output. This approach facilitates the flow of gradients during backpropagation, allowing for more stable and efficient training, even in deeper networks. The ResNet-18 architecture consists of 18 layers with learnable weights, including four residual blocks, each comprising two convolutional layers. As the network progresses through these blocks, the number of feature channels increases--from 64 in the initial layers to 512 in the final residual block--enabling the extraction of increasingly complex features from the input images. At the end of the architecture, a global average pooling layer reduces the spatial dimensions of the feature maps, followed by a fully connected classification layer that produces the final output (Figure 13). This structure is particularly well-suited for image classification tasks, as it balances model depth with computational efficiency, making it appropriate for applications involving moderately sized datasets, such as those used in this work. ResNet-18's ability to preserve important spatial and semantic features while maintaining training stability makes it an effective choice for the task of detecting embedded reinforcement patterns in thermographic data. The model training process was carried out over 10 epochs, during which the binary classification network was iteratively optimized using the simulation dataset. The training resulted in high classification accuracy, consistently exceeding 90%, which indicates the model's strong ability to distinguish between the two target classes. The final size of the trained model is approximately 40 MB, which makes it compact enough for practical deployment while preserving predictive performance. To illustrate the learning dynamics and generalization behavior, the training and validation curves are presented in Figure 14. These curves reflect the evolution of the loss function and accuracy metrics over successive epochs. Furthermore, the confusion matrix obtained on the training data is shown in Figure 15, providing a detailed overview of the model's classification outcomes. Following the training phase conducted exclusively on numerically simulated data, the trained model was subsequently evaluated on experimental thermal images to assess its generalization capability and practical effectiveness. As presented in Sections 3 and 4, the experimental data were obtained from two separate measurement setups, which differed in the inclination angle of the antenna relative to the sample surface. Accordingly, two scenarios are analyzed: one with an inclination angle of 30 degrees, and another with 45 degrees. To maintain clarity throughout the discussion, these configurations will be referred to as \"Experiment Scenario 30\" and \"Experiment Scenario 45\", respectively. Figure 13: ResNet-18 architecture. For the thermal images obtained from experiments, a classification procedure was carried out using a sliding window approach. Starting from the left edge of the image, a fragment of fixed size (8 \(\times\) 239 pixels) was extracted. For each such fragment, a prediction was generated using the previously trained binary classification model. The output of the model at each position was interpreted as the probability of the presence of a reinforcement bar within the corresponding window.