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A Survey of Modern Deep Learning based Object Detection Models

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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

Citation

Zaidi, S., Ansari, M., Aslam, A., Kanwal, N., Asghar, M., & Lee, B. (2022). A Survey of Modern Deep Learning based Object Detection Models. Digital Signal Processing, 1-19. https://doi.org/10.1016/j.dsp.2022.103514

Acceptance Date Apr 24, 2021
Online Publication Date Mar 8, 2022
Publication Date Jun 30, 2022
Publicly Available Date Mar 9, 2024
Journal Digital Signal Processing
Print ISSN 1051-2004
Electronic ISSN 1095-4333
Publisher Elsevier
Pages 1-19
DOI https://doi.org/10.1016/j.dsp.2022.103514
Public URL https://keele-repository.worktribe.com/output/421409
Publisher URL https://www.sciencedirect.com/science/article/pii/S1051200422001312?via%3Dihub

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