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dc.contributor.advisorHays, James
dc.contributor.authorMoore, Lawrence S.
dc.date.accessioned2017-07-28T18:33:30Z
dc.date.available2017-07-28T18:33:30Z
dc.date.created2017-08
dc.date.submittedAugust 2017
dc.identifier.urihttp://hdl.handle.net/1853/58488
dc.description.abstractMosquitoes are directly responsible for the death of more than a million people each year. Yet the ability to mitigate their deadly impact or even monitor them in the wild to better understand their behavior remains relatively limited. One of the primary reasons for this lack of progress is the difficulty in locating and tracking an individual mosquito, leading to only estimates for a population as a whole. To address this problem, this research discusses several approaches using computer vision to detect and track the flight of mosquitoes. In particular, we discuss the performance of several convolutional neural network architectures which show promising results. Once these techniques are refined to give a high enough degree of accuracy, this vision system could be used in conjunction with drones to track and eliminate mosquitoes in both an indoor and outdoor setting.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectComputer vision
dc.subjectMachine learning
dc.subjectNeural networks
dc.titleDetecting Mosquitoes with Convolutional Neural Networks
dc.typeUndergraduate Research Option Thesis
dc.description.degreeUndergraduate
dc.contributor.departmentComputer Science
thesis.degree.levelUndergraduate
dc.contributor.committeeMemberHu, David
dc.date.updated2017-07-28T18:33:30Z


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