## 6 Summary and Conclusions  
This study introduces an augmented dataset that integrates both real and synthetic ballast data to improve the performance of deep learning models in the segmentation and evaluation of railroad ballast images. Details on the collection, processing, and annotation of the real ballast dataset were presented first in the paper, demonstrating how a high-quality real ballast image dataset could be established to effectively train deep learning models in practical applications. To address the inherent limitations of relying solely on real ballast data, an automated workflow for synthesizing ballast data was developed. Two distinct experiments were carried out to assess the effectiveness of the synthetic ballast dataset, which was generated using specific scene generation and realistic rendering techniques. Nevertheless, the segmentation model trained solely with the synthetic ballast dataset underperformed that trained with the real dataset, indicating that gaps existed between the real data and generated synthetic data. To fill such gaps, it was worth investigating how the current data synthesis workflow could be improved, especially through integrating advanced techniques like domain randomization [45]. There are also opportunities to apply deep learning-based data augmentation methods, such as GANs [19], to make the fake images more similar to the real field images. This progress can be crucial for future research studies that rely on synthetic datasets. Conversely, segmentation models trained on the augmented dataset--comprising both real and synthetic data--demonstrated improved precision and enhanced capabilities beyond those trained solely on real data. This approach underscores the advantages of integrating synthetic data to augment real datasets for improving model performance. The proposed workflow for synthesizing ballast data also sets the stage for future research endeavors, including the development of 3D models for ballast particle segmentation, shape completion, and morphological analysis. These advancements aim to support more effective ballast inspection and investigative methodologies to facilitate optimal ballast maintenance strategies. Figure 10: Segmentation results on a real ballast image. (**a**) Ballast segmentation results obtained from the model trained exclusively on real ballast images and (**b**) zoom-in view. (**c**) Ballast segmentation results derived from the model trained using augmented ballast images and (**d**) zoom-in view. Different colors indicate different ballast particles detected. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment** & **Trained with Real** & **Trained with Synthetic** & \multirow{2}{*}{\(mAP_{max}\)} & \multirow{2}{*}{\(F1_{max}\)} \\
**Group** & **Ballast Dataset** & **Ballast Dataset** & & \\ \hline TTRR & YES & NO & 32.1\% & 34.3\% \\ TSTR & NO & YES & 21.2\% & 23.9\% \\
**TATR** & **YES** & **YES** & **32.9\%** & **34.6\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics on the real ballast test dataset for models trained with real ballast dataset (TRTR), synthetic ballast dataset (TSTR), and augmented ballast dataset (TATR). **Author Contributions:** The authors confirm their contribution to the paper as follows: conceptualization, E.T., J.M.H., K.D., J.L., H.H. and I.L.A.Q. ; data collection, K.D., J.L., H.H. and J.M.H. ; methodology, K.D. ; validation, K.D. ; writing--original draft preparation, K.D. ; writing--review and editing, K.D., I.A.Q., E.T., J.M.H., J.L. and H.H. ; supervision, E.T. and J.M.H. ; funding acquisition, E.T. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** The authors would like to acknowledge the China Scholarship Council (CSC) scholarship, which provided funding for [PERSON]'s time and research effort in this paper.