Zaidi, SSA, Ansari, MS, Aslam, A, Kanwal, N ORCID: https://orcid.org/0000-0002-9732-3126, Asghar, M and Lee, B (2021) A Survey of Modern Deep Learning based Object Detection Models. IET Computer Vision. (In Press)

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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

Item Type: Article
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website.
Subjects: T Technology > T Technology (General)
Z Bibliography. Library Science. Information Resources > ZA Information resources
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 02 Nov 2021 11:05
Last Modified: 02 Nov 2021 11:05
URI: https://eprints.keele.ac.uk/id/eprint/10210

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