## 1 Introduction
However, a key limitation remains: the interpretation of thermal data still relies heavily on expert knowledge and manual analysis. As reinforcement layouts become more complex, or signals become noisy, traditional approaches become less practical for rapid or large-scale inspections. To address this challenge, the present study proposes a novel framework that combines active microwave thermography with deep learning-based image analysis for the detection and localization of reinforcement bars in concrete. Artificial neural networks are increasingly employed to support and enhance non-destructive testing (NDT) techniques by enabling automated interpretation of complex data and improving the reliability of defect detection [28; 29; 30]. The core objective of this study is to test the possibility of automating the interpretation of thermal patterns induced by microwave excitation, allowing reliable identification of internal steel elements without surface contact or destructive access. A major innovation of this work is the use of numerical simulations to generate synthetic thermal image datasets. These simulations model the propagation of microwave fields through concrete, the indirect heating effects around steel rebars, and the resulting surface temperature distributions under both continuous and modulated excitation. A large set of synthetic thermograms was created for diverse reinforcement configurations and material properties. The simulated data was used to train a deep convolutional neural network (CNN) based on the ResNet-18 architecture, a residual network known for its strong performance in pattern recognition tasks with limited training data [31; 32; 33]. The use of numerically generated data in the training process offers several key advantages. First, it enables the creation of large, diverse, and precisely labeled datasets that would be prohibitively time-consuming or impractical to obtain through physical experiments alone. This includes full control over variables such as rebar spacing, depth, inclination, and concrete properties, allowing the network to learn from a broad range of scenarios and improve its generalization capability. Second, numerical simulations are noise-free and repeatable, which supports stable and efficient convergence during network training. Third, synthetic data can be systematically augmented and adapted to test specific hypotheses or sensor configurations, making the approach highly flexible and scalable. Moreover, simulation-based training can serve as a valuable tool in early-stage development, where experimental data is limited or unavailable. Finally, by modeling the underlying physics of microwave heating and thermal responses, numerical datasets can expose the neural network to subtle but physically meaningful patterns that may be difficult to isolate in empirical data alone. The network was designed to detect and localize steel bars within thermal images, learning to recognize the subtle features associated with rebar-induced heat accumulation and electromagnetic effects. Following training, the model was evaluated on a separate set of experimental thermal images, acquired from real concrete specimens with known rebar layouts, heated using microwave excitation under controlled conditions. Detection accuracy was assessed using the F1-score, a harmonic mean of precision and recall that provides a balanced measure of binary classification performance. In the best test cases, the network achieved F1-scores exceeding 0.9, demonstrating excellent agreement with ground truth and confirming the effectiveness of the simulation-based training approach. The step-by-step approach employed in this research is presented in Figure 1.  
In summary, this study presents a data-driven methodology for non-contact, automated detection of reinforcement bars in concrete using thermal imaging under microwave excitation. The approach addresses limitations of traditional NDT techniques by leveraging  
Figure 1: The schematic representation of the research process.