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

作     者：Narayanan Ganesh Rajendran Shankar Miroslav Mahdal Janakiraman SenthilMurugan Jasgurpreet Singh Chohan Kanak Kalita 

作者机构：School of Computer Science and EngineeringVellore Institute of TechnologyChennai600127India Department of Computer Science and EngineeringKoneru Lakshmaiah Education FoundationVaddeswaram522502India Department of Control Systems and InstrumentationFaculty of Mechanical EngineeringVSB-Technical University of OstravaOstrava70800Czech Republic Department of Computer Science and EngineeringVel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Mechanical Engineering and University Centre for Research&DevelopmentChandigarh UniversityMohali140413India Department of Mechanical EngineeringVel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadi600062India 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第139卷第4期

页      面：103-141页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0701[理学-数学] 

基　　金：supported by the Project SP2023/074 Application of Machine and Process Control Advanced Methods supported by the Ministry of Education Youth and Sports Czech Republic 

主　　题：Neural network machine vision classification object detection deep learning 

摘      要：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 *** learning(DL)methods are more successful than other traditional machine learning(ML)methods *** techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face *** this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is *** sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and *** 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.