Award Date


Degree Type


Degree Name

Master of Science in Computer Science


Computer Science

First Committee Member

Fatma Nasoz

Second Committee Member

Brendan Morris

Third Committee Member

Beiyu Lin

Fourth Committee Member

Venkata Prashant Modekurthy

Fifth Committee Member

Venkatesan Muthukumar


In the past few years, computer vision has made huge jumps due to deep learning which leverages increased computational power and access to data. The computer vision community has also embraced transparency to accelerate research progress by sharing open datasets and open source code. Access to large scale datasets and benchmark challenges propelled and opened the field. The autonomous vehicle community is a prime example. While there has been significant growth in the automotive vision community, not much has been done in the rail domain. Traditional rail inspection methods require special trains that are run during down time, have sensitive sensing/imaging analysis equipment with high costs, or may require low speeds for analysis. In addition, the lack of available labeled datasets for the rail domain has limited progress in the field. In this thesis, we explored and evaluated machine learning algorithms for real-time railroad inspection systems from the ego-perspective of the locomotive. This was accomplished through a study on state-of-the-art semantic segmentation models on popular automotive datasets with semantic segmentation annotations. Second, transfer learning was performed on the models with a public rail dataset. Third, benchmarking was done on the newly trained rail models on an embedded system and PC. Finally, a custom dataset was created to highlight anomalies on rails (e.g., mud pumping and vegetation).


Anomaly Detection; Computer Vision; Machine Learning; Railway; Real-Time Semantic Segmentation; Semantic Segmentation


Computer Sciences

File Format


File Size

64700 KB

Degree Grantor

University of Nevada, Las Vegas




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