CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope

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 producin...

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Bibliographic Details
Published in:	Electronics (Basel) 2021-10, Vol.10 (20), p.2470
Main Authors:	Bhatt, Dulari, Patel, Chirag, Talsania, Hardik, Patel, Jigar, Vaghela, Rasmika, Pandya, Sharnil, Modi, Kirit, Ghayvat, Hemant
Format:	Article
Language:	eng
Subjects:	Algorithms Architecture Artificial neural networks attention-based CNN CNN Computer Science Computer vision Computers Data processing Datavetenskap deep CNN Deep learning Design Exploitation feature-map exploitation Image processing Image segmentation Investigations Literature reviews Natural language processing Neural networks Object recognition Optimization Regularization Spatial data Speech recognition Taxonomy Trends Video data
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Summary:	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.
ISSN:	2079-9292 2079-9292