Open Access

REVIEW

Exploring Deep Learning Methods for Computer Vision Applications across Multiple Sectors: Challenges and Future Trends

by Narayanan Ganesh¹, Rajendran Shankar², Miroslav Mahdal³, Janakiraman Senthil Murugan⁴, Jasgurpreet Singh Chohan⁵, Kanak Kalita^6,*

1 School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, 600 127, India
2 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, 522502, India
3 Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, Ostrava, 708 00, Czech Republic
4 Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600 062, India
5 Department of Mechanical Engineering and University Centre for Research & Development, Chandigarh University, Mohali, 140413, India
6 Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, 600 062, India

* Corresponding Author: Kanak Kalita. Email:

Computer Modeling in Engineering & Sciences 2024, 139(1), 103-141. https://doi.org/10.32604/cmes.2023.028018

Received 26 November 2022; Accepted 05 September 2023; Issue published 30 December 2023

Abstract

Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV. This review will provide readers with context and examples of how these techniques can be applied to specific areas. A curated list of popular datasets and a brief description of them are also included for the benefit of readers.

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Keywords

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