## 4 Discussion  
The challenge in developing algorithms using artificial intelligence often lies in providing high-quality datasets. Particularly, in detecting objects buried in railway beds, the available datasets are not sufficient in either quality or quantity. This is partly due to the fact that the verification of manually labeled data would require extensive excavation work, which seems highly impractical given the necessary volume of data. The primary aim of this project is to create an automated process for generating physically accurate data, labeling them, and subsequently applying algorithms that have been trained on this dataset on real-world environments. To achieve the objective, a digital twin simulating the electromagnetic properties of a real GPR antenna system was developed and integrated into a simulation environment tailored for the railway sector. This allows for the simulation of measurements in customizable railway scenarios, providing realistic data and ensuring that ground truth is available for subsequent training steps. The processing of simulated radar data was undertaken by using two distinct methods, with a focus on the additional insights gained from utilizing all channels of a multi-antenna system. These data were transformed into RGB images, where each color channel conveyed different information, enabling direct training with the YOLO framework. The detection model was demonstrated to be capable of identifying objects within the railway bed by using exclusively synthetic training data. The performance of the model and its transferability to real-world data were evaluated through measurements on a reference track, where known objects had been buried in advance. These measurements were then compared with the predictions of the model to assess accuracy and reliability. This work represents a significant advance in the application of artificial intelligence in the railway sector by overcoming the challenge of high-quality training data scarcity through the use of realistic simulation models by leveraging the digital twin of an multi-antenna system. The created simulation environment effectively generates realistic GPR measurements for various railway environments and subsoil conditions, avoiding the need for the costly gathering of labeled real-world data. This advancement not only simplifies future research in object detection and beyond but also introduces a novel method for transferring the three-dimensional radar data of a multi-antenna system, enabling the efficient use of all system channels within established detection frameworks, marking a novel application in the railway industry. Although the feasibility of developing detection algorithms exclusively with artificial data has been demonstrated, the limitations of this approach must be acknowledged. Firstly, the results might not be universally applicable to other antennas due to the customization of the digital twin to a specific antenna. Additionally, a risk of overfitting was indicated by the restricted dataset used in this study, suggesting that an expansion of the dataset should be aimed for in future research to enhance reliability. It is also acknowledged that the performance of various detection frameworks may vary, with some potentially outperforming the chosen YOLO framework. Therefore, identifying the most suitable detection framework necessitates comparative testing. Moreover, the applicability of the chosen processing methods to objects with varying radar-reflection characteristics remains uncertain. While the differentiation between two types of objects (steepers and buried containers) has been successfully achieved, the efficacy in distinguishing among various buried objects, such as metal containers and water-filled boxes, has yet to be tested. Lastly, the basis for the evaluation of model performance has been a singular reference measurement, which may not accurately reflect the breadth of potential application contexts.