## 6 Conclusions  
This study presented a novel approach for the detection and localization of steel reinforcement bars in concrete structures using active microwave thermography supported by deep learning techniques. A hybrid methodology was proposed, combining numerical modeling of the microwave heating process with the training of a convolutional neural network to enable automated and robust interpretation of thermal images. The numerical models were developed to simulate heat distribution in reinforced concrete elements under both continuous and modulated microwave excitation, with different angular orientations between the sample and the antenna. These simulations provided a diverse and controlled dataset of thermographic responses, which served as the training base for a ResNet-18 neural network. The trained model was subsequently evaluated on experimental data acquired under two distinct physical configurations, referred to as Experiment Scenario 30 and Experiment Scenario 45. The detection performance was assessed using the F1-score metric. The results indicate that the model is capable of achieving high detection accuracy, with F1-scores exceeding 0.9 in favorable conditions. Notably, Experiment Scenario 45 consistently yielded superior results, both in terms of average performance and repeatability. In contrast, Scenario 30 exhibited greater variability and a higher incidence of missed detections, particularly when thermal contrast was reduced or the spatial alignment of bars was suboptimal. The findings confirm that the integration of numerical thermography with deep learning offers a promising path toward efficient, non-contact evaluation of internal concrete reinforcement. The proposed methodology enables automated processing of thermographic data, reducing the need for manual interpretation and increasing the objectivity of assessments. A distinctive feature of this work is the use of large-scale concrete slabs (1 m \(\times\) 1 m \(\times\) 65 mm) containing embedded reinforcement, which reflects a more realistic structural context compared to prior studies focused mainly on simple specimens with single rebars. This experimental setup allowed for testing the approach under conditions that more closely approximate field applications, introducing spatial complexity, edge effects, and non-uniform heating that are typical in real structures. Future work will focus on expanding the diversity of simulated training data, improving robustness to noise and structural disturbances, and extending the approach to multi-class classification, such as the estimation of bar diameter or cover thickness. In addition, further experimental campaigns are planned to include more complex and volumetric reinforcement arrangements--such as orthogonal meshes and spatially distributed bars--as well as alternative reinforcement materials, including fiber-reinforced polymer (FRP) bars. These efforts will aim to validate and generalize the proposed method across a broader range of concrete reinforcement scenarios. Figure 19: Visualization of F1-score distributions obtained for Experiment Scenario 30 (Exp 1) and Experiment Scenario 45 (Exp 2). **Author Contributions:** Conceptualization, B.S., M.K. and S.A.K. ; methodology, B.S. and M.K. ; software, B.S. and M.K. ; validation, B.S. and M.K., formal analysis, B.S. and M.K. ; investigation, B.S., M.K. and S.A.K. ; resources, B.S., M.K., S.A.K., F.B. and D.D. ; data curation, B.S. and M.K. ; writing--original draft preparation, B.S. and M.K. ; writing--review and editing, B.S. and M.K. ; visualization, B.S. and M.K. ; supervision, B.S., S.A.K., D.D. and F.B. ; project administration, B.S., S.A.K., D.D. and F.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.