###### Abstract  
Ground-penetrating radar (GPR) is a non-invasive technology that uses electromagnetic pulses for subsurface exploration. In the railroad sector, it is crucial to assessing soil layers and infrastructure, offering insights into soil stratification and geological features and aiding in identifying subsurface hazards. However, the automation of radargram analysis is impeded by the lack of ground truth--accurate real-world data used to validate machine learning models--thus affecting the deployment of advanced algorithms. This study focuses on generating high-quality simulated data to address the shortage of real-world data in the context of object detection along railroad tracks and presents a fully automated pipeline that includes data generation, algorithm training, and validation using real-world data. By doing so, it paves the way for significantly easing the future task of object detection algorithms in the railway sector. A simulation environment, including the digital twin of a GPR antenna, was developed for artificial data generation. The process involves pre- and post-processing techniques to transform the three-dimensional data from the multichannel GPR system into two-dimensional datasets. This ensures minimal information loss and suitability for established two-dimensional object detection algorithms like the well-known YOLO (You Only Look Once) framework. Validation involved real-world measurements on a track with predefined buried objects. The entire pipeline, encompassing data generation, processing, training, and application, was automated for efficient algorithm testing and implementation. Artificial data show promise for better performance with increased training. Future AI and sensor advancements will enhance subsurface exploration, contributing to safer and more reliable railroad operations. GPR; object detection; radargram; radar Article  
11 March 20  
Despite its extensive utility, the application of object detection in GPR is not without challenges. Existing learning algorithms for object detection, which have seen success in various domains, rely heavily on manually labeled data from real measurements [3; 4; 5]. This approach necessitates prior knowledge of the subsurface, a requirement often unmet in the context of object detection beneath railway tracks. While some data for soil layer analysis might be partially labeled, providing ground truth, the same is not true for object detection. The manual labeling of such data would be labor-intensive and impractical, as verification would require extensive excavation. To address these challenges, an innovative approach that does not rely on manually labeled data is proposed in this work, where physics-based simulation data are leveraged to fill this gap. Simulated data have already been used in the verification of processing pipelines, for example, in the detection of objects in tunnel linings [5], but models applied to real data require training on labeled real-world samples. While the effectiveness of enhancing detection models with simulated data has also been previously established [6], manual efforts are rendered completely unnecessary in this approach. Even though success has been shown by non-physics-based methods, particularly those employing Generative Adversarial Networks (GANs), for tasks like radargram de-noising [7], they have largely been confined to two-dimensional applications [8; 9]. The rich information available in multi-antenna GPR systems, particularly vital to detecting objects where significant signal variations across channels are observed, is neglected by such methods. The aim of this paper is to harness such additional channel information to enhance object detection models in railroad track beds. The generation of high-quality training data also requires substantial fine tuning to achieve optimal results.