Repetitive assembly basic action detection and standard work measurement based on deep learning

IF 3.7 3区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

International Journal of Computer Integrated Manufacturing Pub Date : 2024-06-29 DOI:10.1080/0951192x.2024.2372281

Nailiang Li, Dan Zhang, Yicong Li, Qi Zhang

引用次数: 0

Abstract

Researchers extensively use deep learning for assembly task action recognition due to its superior feature representation. However, current methods fail to integrate assembly actions with basic hum...

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基于深度学习的重复性装配基本动作检测和标准工作测量

由于深度学习具有卓越的特征表示能力，研究人员广泛将其用于装配任务的动作识别。然而，目前的方法无法将装配动作与基本的人类活动结合起来。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Computer Integrated Manufacturing 工程技术-工程：制造

CiteScore

9.00

自引率

9.80%

发文量

审稿时长

10 months

期刊介绍： International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing. Terms such as industry 4.0, intelligent manufacturing, digital manufacturing and cyber-physical manufacturing systems are now used to identify the area of knowledge that IJCIM has supported and shaped in its history of more than 30 years. IJCIM continues to grow and has become a key forum for academics and industrial researchers to exchange information and ideas. In response to this interest, IJCIM is now published monthly, enabling the editors to target topical special issues; topics as diverse as digital twins, transdisciplinary engineering, cloud manufacturing, deep learning for manufacturing, service-oriented architectures, dematerialized manufacturing systems, wireless manufacturing and digital enterprise technologies to name a few.