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dc.contributor.authorKapur, Salil Vishnu
dc.date.accessioned2019-08-22T12:37:53Z
dc.date.available2019-08-22T12:37:53Z
dc.identifier.urihttp://hdl.handle.net/10222/76281
dc.description.abstractThe research proposes an autonomous solution using deep learning techniques inte- grated on unmanned aerial vehicles (UAV) to effect a rapid and timely search in case of a man overboard (MOB) where a person has accidentally fallen off a ship. The UAV would be deployed from a ship. A light weight neural network model is to be in- tegrated on a drone with limited processing resources. An image dataset was collected using open source videos based on people in the water (positive) and their negatives, object cropping was done by considering RGB spectrum. Three deep learning archi- tectures have been developed considering the trade-off between the minimum memory utilization (efficiency) and maximum accuracy (efficacy). It is hypothesized that a small network can achieve results equivalent to a larger network when chosen on the efficacy/efficiency trade-off. On experimental evaluation, the smaller numbers of pa- rameters yield the best F 1score performance. Neither the very large 379.7 MB ( 94 million parameters) nor very small networks 1.8 MB ( 0.03 million parameters) gave the best performance; rather the mid-size network 48.5 MB ( 12 million parameters) achieved best results.en_US
dc.language.isoenen_US
dc.subjectcomputer visionen_US
dc.subjectCNNen_US
dc.subjecttransfer learningen_US
dc.subjectdistillationen_US
dc.subjectlight weight neural networksen_US
dc.subjecthuman detectionen_US
dc.subjectdroneen_US
dc.subjectnvidia jetson TX2en_US
dc.subjectroboticsen_US
dc.subjectdeep learningen_US
dc.titleMARINE SEARCH AND RESCUE USING LIGHT WEIGHT NEURAL NETWORKSen_US
dc.date.defence2019-08-01
dc.contributor.departmentFaculty of Computer Scienceen_US
dc.contributor.degreeMaster of Computer Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.graduate-coordinatorMike McAlisteren_US
dc.contributor.thesis-readerEvangelos E. Miliosen_US
dc.contributor.thesis-readerFernando V. Paulovichen_US
dc.contributor.thesis-supervisorStan Matwinen_US
dc.contributor.thesis-supervisorMae Setoen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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