Multi-Scale Object Detection in Aerial Images with Feature Pyramid Networks

dc.contributor.advisorRad, Paul
dc.contributor.advisorHuang, Yufei
dc.contributor.authorBhattarai, Sujan
dc.contributor.committeeMemberBanerjee, Taposh
dc.creator.orcidhttps://orcid.org/0000-0001-6475-4926
dc.date.accessioned2024-02-09T19:29:41Z
dc.date.available2024-02-09T19:29:41Z
dc.date.issued2018
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.abstractThe field of Computer Vision has seen rapid development with the rise of the Artificial Neural Networks. There are have a been a plethora of scientific research and publications of Computer vision algorithms using Deep Learning after the introduction of AlexNet in the 2012 ImageNet Competition. We are witnessing new Algorithms and Architectures that are beating the existing benchmark scores year after year, some even surpassing human accuracy levels in tasks like image classification all thanks to Neural N etworks. Although these algorithms are being praised for there capabilities, one can also observe that most of the benchmark are set in the standard Competition Datasets like ImageNet. The images in a typical bench-marking dataset might not always be representative of the real world. There are a lot of post-processing and quality control applied to these images. As such, we wanted to explore the applications and performance of existing Deep Learning Architectures on a dataset that is much more representative of the real world. We focused our work on the xView Dataset. This is an object detection dataset. The images in this dataset are representative of the real world for a number of regions. One could ask why Aerial Images in particular? : its because not much literature is available on niche of Machine Learning & Deep Learning on Aerial Images. With the increasing number of commercial satellites, the potential of Automatic information retrieval capabilities is huge. We used two popular object detection Algorithms : Faster R-CNN and SSD. We also made use of a Feature Pyramid Network(FPN) based classifier that we found to be well suited to the task of object detection in a dataset with large variation of object sizes. We show experimental results that show FPN back-boned network to perform better than a typical Vanilla back-boned network.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent44 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9780438743038
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2985
dc.languageen
dc.subjectComputer Vision
dc.subjectDeep Learning
dc.subjectObject Detection
dc.subjectxview dataset
dc.subject.classificationComputer engineering
dc.subject.classificationElectrical engineering
dc.titleMulti-Scale Object Detection in Aerial Images with Feature Pyramid Networks
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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