Vehicle Detection in Deep Learning

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dc.contributor.authorXiao, Yaoen
dc.contributor.committeechairAbbott, A. Lynnen
dc.contributor.committeememberBuehrer, R. Michaelen
dc.contributor.committeememberPillis, Danielen
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2019-07-09T08:00:30Zen
dc.date.available2019-07-09T08:00:30Zen
dc.date.issued2019-07-08en
dc.description.abstractComputer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the-art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle detection are developing techniques to solve this problem. This thesis proposes an advanced vehicle detection model, adopting one of the classical neural networks, which are the residual neural network and the region proposal network. The model utilizes the residual neural network as a feature extractor and the region proposal network to detect the potential objects' information.en
dc.description.abstractgeneralComputer vision techniques are becoming increasingly popular. For example, face recognition is used to help police find criminals, vehicle detection is used to prevent drivers from serious traffic accidents, and written word recognition is used to convert written words into printed words. With the rapid development of vehicle detection given the use of deep learning techniques, there are still concerns about the performance of state-of-the art vehicle detection techniques. For example, state-of-the-art vehicle detectors are restricted by the large variation of scales. People working on vehicle detection are developing techniques to solve this problem. This thesis proposes an advanced vehicle detection model, utilizing deep learning techniques to detect the potential objects’ information.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:20575en
dc.identifier.urihttp://hdl.handle.net/10919/91375en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectVehicle Detectionen
dc.subjectDeep learning (Machine learning)en
dc.subjectConvolutional Neural Networksen
dc.subjectImage Processingen
dc.subjectArchitecture Designen
dc.titleVehicle Detection in Deep Learningen
dc.typeThesisen
thesis.degree.disciplineComputer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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