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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN’s components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction.

Details

Title
CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope
Author
Bhatt, Dulari 1 ; Patel, Chirag 2 ; Talsania, Hardik 1 ; Patel, Jigar 1 ; Vaghela, Rasmika 1 ; Pandya, Sharnil 3   VIAFID ORCID Logo  ; Modi, Kirit 4   VIAFID ORCID Logo  ; Ghayvat, Hemant 5 

 Research Scholar, Parul University, Gujarat 382030, India; dulari.bos@gmail.com (D.B.); hardik.talsania@gujgov.edu.in (H.T.); jigarsharp@gmail.com (J.P.); rashmika.vaghela@gujgov.edu.in (R.V.) 
 Computer Science & Engineering, DEPSTAR, Changa, Gujarat 388421, India 
 Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India; sharnil.pandya@sitpune.edu.in 
 Sankalchand Patel College of Engineering, Sankalchand Patel University, Visnagar 384315, India; kjmodi.fet@spu.ac.in 
 Computer Science Department, Faculty of Technology, Linnaeus University, P G Vejdes väg, 351 95 Växjö, Sweden; hemant.ghayvat@lnu.se 
First page
2470
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20799292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2584602860
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.