Detection of waste containers using computer vision

Valente, Miguel; Silva, Hélio; Caldeira, J.M.L.P.; Soares, V.N.G.J.; Gaspar, Pedro Dinis

Publication

Detection of waste containers using computer vision

2019Journal article

dc.contributor.author	Valente, Miguel
dc.contributor.author	Silva, Hélio
dc.contributor.author	Caldeira, J.M.L.P.
dc.contributor.author	Soares, V.N.G.J.
dc.contributor.author	Gaspar, Pedro Dinis
dc.date.accessioned	2019-07-23T14:10:52Z
dc.date.available	2019-07-23T14:10:52Z
dc.date.issued	2019
dc.description.abstract	This work is a part of an ongoing study to substitute the identification of waste containers via radio-frequency identification. The purpose of this paper is to propose a method of identification based on computer vision that performs detection using images, video, or real-time video capture to identify different types of waste containers. Compared to the current method of identification, this approach is more agile and does not require as many resources. Two approaches are employed, one using feature detectors/descriptors and other using convolutional neural networks. The former used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures used on the test set.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	VALENTE, Miguel [et al.] (2019) - Detection of waste containers using computer vision. Applied System Innovation. ISSN 2571-5577. Vol. 2, nº 1, p. 1-13	pt_PT
dc.identifier.doi	10.3390/asi2010011	pt_PT
dc.identifier.issn	2571-5577
dc.identifier.uri	http://hdl.handle.net/10400.11/6622
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	MDPI	pt_PT
dc.relation	Instituto de Telecomunicações
dc.relation.publisherversion	https://www.mdpi.com/2571-5577/2/1/11	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Waste container	pt_PT
dc.subject	Object detection	pt_PT
dc.subject	VLAD	pt_PT
dc.subject	Convolutional neural networks	pt_PT
dc.subject	YOLO	pt_PT
dc.title	Detection of waste containers using computer vision	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.awardTitle	Instituto de Telecomunicações
oaire.awardURI	info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UID%2FEEA%2F50008%2F2019/PT
oaire.citation.endPage	13	pt_PT
oaire.citation.startPage	1	pt_PT
oaire.citation.title	Applied System Innovation	pt_PT
oaire.citation.volume	2	pt_PT
oaire.fundingStream	6817 - DCRRNI ID
person.familyName	Silva
person.familyName	Caldeira
person.familyName	Gaspar
person.givenName	Hélio
person.givenName	João
person.givenName	Pedro Dinis
person.identifier	a4GD8aoAAAAJ
person.identifier.ciencia-id	A91B-85B8-C27E
person.identifier.ciencia-id	5B19-E130-E382
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person.identifier.orcid	0000-0003-3327-5142
person.identifier.orcid	0000-0001-5830-3790
person.identifier.orcid	0000-0002-8057-5474
person.identifier.orcid	0000-0003-1691-1709
person.identifier.rid	N-3016-2013
person.identifier.scopus-author-id	27067580500
person.identifier.scopus-author-id	57419570900
project.funder.identifier	http://doi.org/10.13039/501100001871
project.funder.name	Fundação para a Ciência e a Tecnologia
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
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