# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 7 Conclusions  
The goal of this research was to develop a robust, accurate, and effective method for identifying ties and ballasts--a crucial step before implementing inspection approaches that rely on differentiating between ties and ballasts in motion. The system studied here uses two downward-facing distance LiDAR sensors on a moving rail cart. The LiDAR sensors measure the differing Doppler effects caused by ties and ballasts.  
Seven techniques were proposed to process the distance of the LiDAR sensors' measurements, starting with the moving standard deviation method aimed at extracting surface figure variations of the track. Despite its simplicity, this method proved unreliable and unsuitable for automated detection. Subsequently, two machine learning algorithms--Logistic Regression and Decision Tree--and three preprocessing methods (inputs) were explored to automatically differentiate between ties and ballasts. Among these six ML models, the one with the Decision Tree algorithm exhibited exceptional accuracy, robustness,  
Figure 19: The final model’s performance: (**a**) The left and right LiDAR sensor measurements, along with the model’s output, in distinguishing between the ties and ballasts over a segment of unseen (i.e., unlabeled) data. The blue line indicates ties (marked as 1) and ballasts (marked as 0). The two ties marked with stars are the same ties shown in Figure 14. (**b**) The confusion matrix shows the percentage of ties and ballasts correctly predicted by the model.  
and efficiency, even at high speeds, mainly when applied to the standard deviation of the difference between the left and right LiDAR sensors' measurements; furthermore, unlike computer vision systems designed for similar purposes, this system requires significantly fewer computational resources, making it more feasible for real-time implementation.  
In conclusion, the developed system integrates two distance LiDAR sensors (left and right) with Decision Tree algorithms applied to the standard deviation of the difference between the sensors' measurements, providing an effective and practical solution for non-contact, in-motion differentiation of ties and ballasts. This system holds the potential to be used onboard Hyrail vehicles, offering a valuable tool for differentiating between ties and ballasts during track inspection or health monitoring, facilitating more targeted and efficient maintenance interventions.  
Methodology, S.M.M., A.R. and C.H.; experiments, S.M.M. and A.R.; analysis, S.M.M. and A.R.; writing--original draft preparation, S.M.M. and M.A.; writing--review and editing, S.M.M., A.R., C.H. and M.A.; supervision, M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the U.S. Department of Transportation, grant number 693J622C000024.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The financial support of the U.S. Department of Transportation and the Federal Railroad Administration's Research, Development and Technology (FRA RD&T) throughout this study is greatly acknowledged. The technical support provided by [PERSON] (FRA), [PERSON] (Volpe), and [PERSON] (Volpe) is also greatly appreciated. We are grateful for Norfolk Southern railroad's support for accommodating the track testing of the LiDAR system.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.