Detection of curbside storm drain from street level images using Faster R-CNN

dc.contributor.advisor Joshua M Peschel
dc.contributor.author Pichaikutty, Prabhakaran
dc.contributor.department Electrical and Computer Engineering
dc.date 2021-01-16T18:24:09.000
dc.date.accessioned 2021-02-25T21:39:11Z
dc.date.available 2021-02-25T21:39:11Z
dc.date.copyright Tue Dec 01 00:00:00 UTC 2020
dc.date.embargo 2020-12-03
dc.date.issued 2020-01-01
dc.description.abstract <p>Stormwater management is a significant part of modern urban infrastructure. With an increase in climate change due to global warming, this system plays a major role in conserving water and maintaining the environment. Also, they play a significant role in risk management in times of flood. Storm Drains/inlets are essential in modeling this system. Precise mapping of the location of these drains is the key step in improving the infrastructure. State of the art deep learning technique using Faster R-CNN is presented in this thesis to identify the drains on the curb of the road using Google street view images as the primary source. The model is evaluated with 1000 street-level images of streets and highways of Urbana-Champaign, Illinois-USA. The method proposed shows a significant improvement in the detection accuracy of drains by eliminating a significant amount of false positives compared to the previous state of the art machine vision detection techniques. The dataset used for the thesis is available for future researchers.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/etd/18377/
dc.identifier.articleid 9384
dc.identifier.contextkey 21104824
dc.identifier.doi https://doi.org/10.31274/etd-20210114-112
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/18377
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/94529
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/etd/18377/Pichaikutty_iastate_0097M_19241.pdf|||Fri Jan 14 21:40:59 UTC 2022
dc.subject.keywords Deep Learning
dc.subject.keywords Urban Infrastructure
dc.title Detection of curbside storm drain from street level images using Faster R-CNN
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff
thesis.degree.discipline Electrical Engineering ( Systems and Controls)
thesis.degree.level dissertation
thesis.degree.name Master of Science
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