dc.contributor.author |
Srirangan, P |
|
dc.date.accessioned |
2022-03-14T07:30:13Z |
|
dc.date.available |
2022-03-14T07:30:13Z |
|
dc.date.issued |
2021 |
|
dc.identifier.citation |
Srirangan, P (2021) VeDDS: Vehicle [car] Detector and Detailing System. BSc. Dissertation Informatics Institute of Technology |
en_US |
dc.identifier.issn |
2017237 |
|
dc.identifier.uri |
http://dlib.iit.ac.lk/xmlui/handle/123456789/953 |
|
dc.description.abstract |
"
Item location is utilized in numerous nations all throughout the planet, on account of a
flood in interest somewhat recently. This paper centers around a dream based
methodology that utilizes a convolutional neural organization for object recognition to
distinguish vehicles progressively. The StandFord dataset is utilized to prepare an
assembled YOLOv4-minuscule model to distinguish vehicles, and the model to the
recognized items. AI is utilized to clarify each period of the preparation cycle, just as
to battle overfitting and improve speed and precision. The creators had the option to
improve the mean normal exactness, which is a measurement for deciding article
finder precision." |
en_US |
dc.language.iso |
en |
en_US |
dc.title |
VeDDS: Vehicle [car] Detector and Detailing System |
en_US |
dc.type |
Thesis |
en_US |