Abstract
Recognition and localization of actions in manufacturing assembly operations improves productivity and product quality by identifying bottlenecks and assembly errors. In our previous work, we developed an approach that can recognize and localize the assembly standard operating procedures (SOP) steps in real-time using vision cameras. In this work, we augment the previous study with the ability to detect objects corresponding to the step being performed. Additionally, identifying non-value-added (NVA) activities in an assembly operation is challenging, hence, in this study, we propose an approach to detecting NVA activities by considering the out-of-distribution for deep learning models.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
