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