A survey of modern deep learning based object detection models
dc.contributor.author | Zaidi, Syed Sahil Abbas | |
dc.contributor.author | Ansari, Mohammad Samar | |
dc.contributor.author | Aslam, Asra | |
dc.contributor.author | Kanwal, Nadia | |
dc.contributor.author | Asghar, Mamoona | |
dc.contributor.author | Lee, Brian | |
dc.date.accessioned | 2022-03-18T15:09:23Z | |
dc.date.available | 2022-03-18T15:09:23Z | |
dc.date.issued | 2022-03-08 | |
dc.identifier | https://chesterrep.openrepository.com/bitstream/handle/10034/626755/A_Survey_of_Modern_Object_Detection_Models___DSP.pdf?sequence=1 | |
dc.identifier.citation | Zaidi, 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.2022.103514 | en_US |
dc.identifier.issn | 1051-2004 | |
dc.identifier.doi | 10.1016/j.dsp.2022.103514 | |
dc.identifier.uri | http://hdl.handle.net/10034/626755 | |
dc.description.abstract | Object 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.publisher | Elsevier | en_US |
dc.relation.url | https://www.sciencedirect.com/science/article/abs/pii/S1051200422001312 | en_US |
dc.rights | Attribution-NonCommercial 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Lightweight networks | en_US |
dc.subject | Convolutional neural networks (CNN) | en_US |
dc.subject | Object detection and recognition | en_US |
dc.title | A survey of modern deep learning based object detection models | en_US |
dc.type | Article | en_US |
dc.contributor.department | Technological University of the Shannon; University of Chester; National University of Ireland; Keele University; Lahore College for Women University | en_US |
dc.identifier.journal | Digital Signal Processing | en_US |
or.grant.openaccess | Yes | en_US |
rioxxterms.funder | Unfunded | en_US |
rioxxterms.identifier.project | Unfunded | en_US |
rioxxterms.version | AM | en_US |
rioxxterms.versionofrecord | 10.1016/j.dsp.2022.103514 | en_US |
rioxxterms.licenseref.startdate | 2024-03-08 | |
dcterms.dateAccepted | 2022-03-02 | |
rioxxterms.publicationdate | 2022-03-08 | |
dc.date.deposited | 2022-03-18 | en_US |
dc.indentifier.issn | 1051-2004 | en_US |