Purpose The purpose of this paper is to explore the role that computer vision can play within new industrial paradigms such as Industry 4.0 and in particular to support production line improvements to achieve flexible manufacturing. As Industry 4.0 requires “big data”, it is accepted that computer vision could be one of the tools for its capture and efficient analysis. RGB-D data gathered from real-time machine vision systems such as Kinect ® can be processed using computer vision techniques. Design/methodology/approach This research exploits RGB-D cameras such as Kinect® to investigate the feasibility of using computer vision techniques to track the progress of a manual assembly task on a production line. Several techniques to track the progress of a manual assembly task are presented. The use of CAD model files to track the manufacturing tasks is also outlined. Findings This research has found that RGB-D cameras can be suitable for object recognition within an industrial environment if a number of constraints are considered or different devices/techniques combined. Furthermore, through the use of a HMM inspired state-based workflow, the algorithm presented in this paper is computationally tractable. Originality/value Processing of data from robust and cheap real-time machine vision systems could bring increased understanding of production line features. In addition, new techniques that enable the progress tracking of manual assembly sequences may be defined through the further analysis of such visual data. The approaches explored within this paper make a contribution to the utilisation of visual information “big data” sets for more efficient and automated production.

中文翻译：

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利用低成本 RGB-D 相机跟踪手动装配序列的实时进度

目的本文的目的是探索计算机视觉在工业 4.0 等新工业范式中可以发挥的作用，特别是支持生产线改进以实现灵活制造。由于工业 4.0 需要“大数据”，因此可以接受计算机视觉可以作为其捕获和有效分析的工具之一。从 Kinect ® 等实时机器视觉系统收集的 RGB-D 数据可以使用计算机视觉技术进行处理。设计/方法/方法 本研究利用 RGB-D 相机（如 Kinect®）来研究使用计算机视觉技术跟踪生产线上手动装配任务进度的可行性。介绍了几种跟踪手动装配任务进度的技术。还概述了使用 CAD 模型文件跟踪制造任务。结果 本研究发现，如果考虑多种限制条件或结合不同的设备/技术，RGB-D 相机可适用于工业环境中的物体识别。此外，通过使用受 HMM 启发的基于状态的工作流，本文提出的算法在计算上是易于处理的。原创性/价值 处理来自强大且廉价的实时机器视觉系统的数据可以增加对生产线特征的理解。此外，可以通过对此类视觉数据的进一步分析来定义能够跟踪手动装配序列进度的新技术。