Transfer Learning For Deep Convolutional Neural Network From Rgb To Ir Domain

Ligocki, Adam; Jelínek, Aleš

Transfer Learning For Deep Convolutional Neural Network From Rgb To Ir Domain

but.event.date	23.04.2020	cs
but.event.title	Student EEICT 2020	cs
dc.contributor.author	Ligocki, Adam
dc.contributor.author	Jelínek, Aleš
dc.date.accessioned	2021-07-15T11:17:22Z
dc.date.available	2021-07-15T11:17:22Z
dc.date.issued	2020	cs
dc.description.abstract	In this paper, we are presenting a proof of concept of our system for training of the YOLOv3 neural network for object detection of vehicles in thermal camera images. Our approach is unique in the way we are using a dataset containing a large number of synchronized range measurements as well as RGB and thermal images. We are using the existing YOLO toolkit to detect objects on the RGB images, we estimate detection distance by the LiDAR and later we reproject these detections into the IR image. In this way, we have created a large dataset of annotated thermal images that helped us to significantly improve the performance of the neural network at the IR domain.	en
dc.format	text	cs
dc.format.extent	366-370	cs
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers. s. 366-370. ISBN 978-80-214-5867-3	cs
dc.identifier.isbn	978-80-214-5867-3
dc.identifier.uri	http://hdl.handle.net/11012/200597
dc.language.iso	en	cs
dc.publisher	Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.relation.ispartof	Proceedings I of the 26st Conference STUDENT EEICT 2020: General papers	en
dc.relation.uri	https://conf.feec.vutbr.cz/eeict/EEICT2020	cs
dc.rights	© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.rights.access	openAccess	en
dc.title	Transfer Learning For Deep Convolutional Neural Network From Rgb To Ir Domain	en
dc.type.driver	conferenceObject	en
dc.type.status	Peer-reviewed	en
dc.type.version	publishedVersion	en
eprints.affiliatedInstitution.department	Fakulta elektrotechniky a komunikačních technologií	cs

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