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dc.contributor.authorZaidi, Syed Sahil Abbas
dc.contributor.authorAnsari, Mohammad Samar
dc.contributor.authorAslam, Asra
dc.contributor.authorKanwal, Nadia
dc.contributor.authorAsghar, Mamoona
dc.contributor.authorLee, Brian
dc.date.accessioned2022-03-18T15:09:23Z
dc.date.available2022-03-18T15:09:23Z
dc.date.issued2022-03-08
dc.identifierhttps://chesterrep.openrepository.com/bitstream/handle/10034/626755/A_Survey_of_Modern_Object_Detection_Models___DSP.pdf?sequence=1
dc.identifier.citationZaidi, S. S. A., Ansari, M. S., Aslam, A., Kanwal, N., Asghar, M., & Lee, B. (2022). A survey of modern deep learning based object detection models. Digital Signal Processing, 126, 103514. https://doi.org/10.1016/j.dsp.2022.103514en_US
dc.identifier.issn1051-2004
dc.identifier.doi10.1016/j.dsp.2022.103514
dc.identifier.urihttp://hdl.handle.net/10034/626755
dc.description.abstractObject Detection is the task of classification and localization of objects in an image or video. It has gained prominence in recent years due to its widespread applications. This article surveys recent developments in deep learning based object detectors. Concise overview of benchmark datasets and evaluation metrics used in detection is also provided along with some of the prominent backbone architectures used in recognition tasks. It also covers contemporary lightweight classification models used on edge devices. Lastly, we compare the performances of these architectures on multiple metrics.en_US
dc.publisherElsevieren_US
dc.relation.urlhttps://www.sciencedirect.com/science/article/abs/pii/S1051200422001312en_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.subjectDeep learningen_US
dc.subjectLightweight networksen_US
dc.subjectConvolutional neural networks (CNN)en_US
dc.subjectObject detection and recognitionen_US
dc.titleA survey of modern deep learning based object detection modelsen_US
dc.typeArticleen_US
dc.contributor.departmentTechnological University of the Shannon; University of Chester; National University of Ireland; Keele University; Lahore College for Women Universityen_US
dc.identifier.journalDigital Signal Processingen_US
or.grant.openaccessYesen_US
rioxxterms.funderUnfundeden_US
rioxxterms.identifier.projectUnfundeden_US
rioxxterms.versionAMen_US
rioxxterms.versionofrecord10.1016/j.dsp.2022.103514en_US
rioxxterms.licenseref.startdate2024-03-08
dcterms.dateAccepted2022-03-02
rioxxterms.publicationdate2022-03-08
dc.date.deposited2022-03-18en_US
dc.indentifier.issn1051-2004en_US


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Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International