Development of a Low Power, Low Cost Rural Railway Intersection Smart Detection and Warning System

dc.contributor.advisorAhmed, Sara
dc.contributor.authorDowning, Raymond
dc.contributor.committeeMemberDessouky, Samer
dc.contributor.committeeMemberAlamaniotis, Miltos
dc.creator.orcidhttps://orcid.org/0000-0002-7757-8308
dc.date.accessioned2024-02-09T20:48:29Z
dc.date.available2022-01-04
dc.date.available2024-02-09T20:48:29Z
dc.date.issued2020
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThis project explores a different approach to provide preemptive warning for train detection at grade-crossings to increase safety and reduce motor vehicle congestion. The development of a novel, low cost, low power, and off rail right-of-way (ROW) detection and warning system will be presented. A background of track circuits, which is the rail industries standard for train detection, will also be provided to highlight the benefits and challenges of the rail industry installing a system at every grade-crossings that lack any type of active warning. The benefits of using thermal imaging instead of traditional video for computer vision will also be discussed. Then, the development of a detection algorithm using convolutional neural networks and deep learning methods on a custom dataset of images created by two different thermal sensors will be overviewed. The two sensors tested are the MLX90640 and FLIR Lepton 3.0. Both react to heat radiation emitted from objects but in very different ways from a technology perspective. This difference in technology creates a large gap in cost and power between the two and was therefore worth testing to analyze the trade off in accuracy achieved in creating an outdoor detection model. Finally, the wireless communication between the detection and warning devices is discussed as well as the network communication for detected data to be logged to an online server and updated on a mobile application. Testing was done at a location approximately one mile east of the Kirby Union Pacific Railyard in San Antonio,TX from a distance that was off the property ROW.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent62 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/3268
dc.languageen
dc.subjectArtificial Intelligence
dc.subjectDeep Neural Network
dc.subjectEmbedded Systems
dc.subjectIntelligent Transportation System
dc.subjectMachine Learning
dc.subjectThermal Vision
dc.subject.classificationEngineering
dc.subject.classificationElectrical engineering
dc.titleDevelopment of a Low Power, Low Cost Rural Railway Intersection Smart Detection and Warning System
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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