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Detection of Waste Containers Using Computer Vision

dc.contributor.authorValente, Miguel
dc.contributor.authorSilva, Hélio
dc.contributor.authorCaldeira, João
dc.contributor.authorSoares, Vasco N. G. J.
dc.contributor.authorGaspar, Pedro Dinis
dc.date.accessioned2019-10-18T14:12:16Z
dc.date.available2019-10-18T14:12:16Z
dc.date.issued2019
dc.description.abstractThis 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.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/asi2010011pt_PT
dc.identifier.issn2571-5577
dc.identifier.urihttp://hdl.handle.net/10400.6/7290
dc.language.isoengpt_PT
dc.publisherMDPIpt_PT
dc.subjectWaste containerpt_PT
dc.subjectObject detectionpt_PT
dc.subjectVLADpt_PT
dc.subjectConvolutional neural networkspt_PT
dc.subjectYOLOpt_PT
dc.titleDetection of Waste Containers Using Computer Visionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleApplied System Innovationpt_PT
person.familyNameSilva
person.familyNameCaldeira
person.familyNameGaspar
person.givenNameHélio
person.givenNameJoão
person.givenNamePedro Dinis
person.identifier.ciencia-id9B19-F708-C33C
person.identifier.ciencia-idA91B-85B8-C27E
person.identifier.ciencia-id6111-9F05-2916
person.identifier.orcid0000-0001-5830-3790
person.identifier.orcid0000-0003-1691-1709
person.identifier.ridN-3016-2013
person.identifier.scopus-author-id27067580500
person.identifier.scopus-author-id57419570900
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationd31cb2db-9b36-44bc-933f-2b0e6b787e46
relation.isAuthorOfPublication431a7461-e862-4b66-86ea-684e274030bf
relation.isAuthorOfPublicationb69e2ba0-43af-4cf7-873e-090fd9fc6c94
relation.isAuthorOfPublication.latestForDiscovery431a7461-e862-4b66-86ea-684e274030bf

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