# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 1 Introduction  
Various studies have aimed at identifying, detecting, and assessing the condition of railroad ties and ballasts, mainly using vision systems or ground-penetrating radars [1, 2, 3, 4, 5]. The emphasis is often placed on how ballast and tie degradation may increase the likelihood of a derailment [6, 7, 8, 9, 10]. Other studies have used alternative technologies for making such assessments, such as the research by [PERSON] et al. that has developed and fabricated a laser-speckle strain sensor to measure the transfer length of concrete ties [11].  
The mentioned techniques are non-contact and have the potential to be mounted onboard Hyrail vehicles or manned and unmanned track geometry cars for autonomous, in-motion assessment of tracks. Their goal is to provide an early warning to track engineers for intervention before any track fault progresses to costly maintenance issues. These studies can be collectively classified as track health condition monitoring research. However, these methods lack the ability to filter out unwanted targets--for example, tie inspection systems often struggle to ignore ballasts, reducing their overall effectiveness.  
The primary purpose of our study was to develop an automated method for differentiating between ties and ballasts, allowing health monitoring systems to focus more accurately on their respective targets during in-motion operations. This study was mainly aimed at distinguishing between ties and ballasts as part of a more extensive study to assess the early stages of track instability from tie vibrations. Obviously, to determine tie vibrations, one needs first to differentiate them from ballasts; hence, this study serves as a preliminary step in developing non-contact, in-motion devices for inspecting ties or even ballasts. The purpose of this system is to serve as an auxiliary unit that enhances the performance of other track stability inspection devices. By providing real-time, high-accuracydifferentiation between ties and ballasts, this system helps to narrow the focus of other inspection tools, enabling them to more effectively target specific track components.  
The existing methods used to distinguish between ties and ballasts are mainly based on ultrasonic or vision methods. For instance, [PERSON] et al. [12] developed an in-motion method for reconstructing the deflection profile of railroad ties by means of non-contact ultrasonic testing. To demarcate the ties, they use a vision-based image classification approach. [PERSON] and [PERSON] [13] also used Digital Image Correlation (DIC) for a similar goal. [PERSON] et al. [5] developed an algorithm based on a deep neural network for semantic segmentation of ties and ballasts and eventually detecting ballast unevenness. Despite these methods' effectiveness, a few issues exist, preventing them from being installed on standard track geometry cars. First, their effectiveness for onboard applications is limited because they are mainly intended for stationary or quasi-static implementation. Second, they require significant post-processing, making them unsuitable for real-time or near-real-time measurements.  
To further advance tie and ballast detection methods, this study evaluated the application of LiDAR-based methods that can be used at speeds suitable for Hyrail vehicle implementation. The following sections will describe the system, its setup on a robotic track cart for technological feasibility evaluation testing, the assessment of the test results through developing new data analysis methods, and the application of the final system.