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A survey of modern deep learning based object detection models

Zaidi, Syed Sahil Abbas
; 
Ansari, Mohammad Samar
; 
Aslam, Asra
; 
Kanwal, Nadia
; 
Asghar, Mamoona
; 
Lee, Brian
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Affiliation
Technological University of the Shannon; University of Chester; National University of Ireland; Keele University; Lahore College for Women University
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Publication Date
2022-03-08
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Computer Science
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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. 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
Publisher
Elsevier
Journal
Digital Signal Processing
Research Unit
URI
http://hdl.handle.net/10034/626755
DOI
10.1016/j.dsp.2022.103514
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PubMed Central ID
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Article
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ISSN
1051-2004
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https://www.sciencedirect.com/science/article/abs/pii/S1051200422001312