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dc.contributor.advisor Tang, Fang en
dc.contributor.author Tellez, Joshua en
dc.date.accessioned 2018-11-08T17:50:49Z en
dc.date.available 2018-11-08T17:50:49Z en
dc.date.issued 2018-11-08 en
dc.identifier.uri http://hdl.handle.net/10211.3/206606 en
dc.description.abstract Neural networks are modern programming structures used by many to produce cutting edge technologies. These cutting-edge technologies can range from advanced medical equipment to self-driving cars. The reason neural networks gained much momentum and became widespread in the world of technology is because they closed some of the difficult gaps that traditional computer science tools and techniques were incapable of quickly solving. These difficult tasks include computer vision, natural language processing, nonlinear prediction problems and the list continues. With the consistent widespread use of neural networks, this project aims to continue the evaluation of neural networks by applying their use on unmanned autonomous vehicles. The focus of this project will be on the computer vision tasks associated with autonomous vehicles and what kinds of neural networks can help accomplish them. The types of neural networks demonstrated in this project include the revolutionary convolutional neural network and the high performing residual neural network. A comparison of their performances on a small custom-made dataset are evaluated and analyzed for their potential on-board use in autonomous unmanned vehicles. en
dc.format.extent 36 pgs. en
dc.language.iso en en
dc.publisher California State Polytechnic University, Pomona en
dc.rights.uri http://www.cpp.edu/~broncoscholar/rightsreserved.html en
dc.subject object detection en
dc.subject neural network en
dc.subject unmanned vehicle en
dc.subject autonomous vehicle en
dc.title Neural Networks for Object Detection with Unmanned Autonomous Vehicles en
dc.type Graduate Project en
dc.contributor.department Department of Computer Science en
dc.description.degree M.S en
dc.contributor.committeeMember Bhandari, Subodh en
dc.rights.license All rights reserved en


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