Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Vishal, S.; Shanthan, Hubert; Vijay Arputharaj, J. Title A name given to the resource Railway Track Crack Detection: A Comparative Study On Yolov7 And U-Net In Automated Inspection Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings of the 2025 International Conference on Computational Innovations and Sustainable Technologies, ICCIST 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCIST67338.2025.11438571" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCIST67338.2025.11438571</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037441912?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037441912?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Vishal S., Christ University, Department of Computer Science, Bengaluru, India; Shanthan H., Christ University, Department of Computer Science, Bengaluru, India; Vijay Arputharaj J., Christ University, Department of Computer Science, Bengaluru, India Description An account of the resource For railway networks to remain operationally safe and avoid catastrophic failures, structural integrity is essential. Track cracks can be found using labor-intensive, slow, and human error-prone manual inspection techniques. In this work, two cutting-edge deep learning models - YOLOv8 andU-Net v2 - for automated railway track crack detection using high-resolution imagery from Unmanned Aerial Vehicles (UAVs) are compared. In a real-world inspection scenario, we compare the different strategies of precise semantic segmentation (U-Net) and real-time object detection (YOLOv8) in order to assess their relative trade-offs. We compare performance on important metrics such as precision, recall, intersection over union (IoU), and inference speed using a custom dataset that was taken by a DJI Matrice 300 RTK drone. This work is novel because it examines how each model's output - bounding boxes versus pixel-level masks - directly affects the usefulness for maintenance workflows from an application-focused perspective. According to our research, U-Net v2 offers the fine-grained information required for precise damage assessment, while YOLOv8 is best suited for quick, extensive screening. This study offers railway operators useful information for creating a multi-stage, hybrid inspection strategy that strikes a balance between accuracy and speed. 2025 IEEE. Subject The topic of the resource Computer Vision; Crack Detection; Deep Learning; DJI Matrice 300 RTK; Drone Inspection; Infrastructure Monitoring; Railway Safety; U-Net v2; UAV; YOLOv8 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833159676-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online