The increasing popularity of Deep Learning (DL) based Object Detection (OD) methods and their real-world applications have opened new venues in smart manufacturing. Traditional industries struck by capacity constraints after Coronavirus Disease (COVID-19) require non-invasive methods for in-depth operations' analysis to optimize and increase their revenue. In this study, we have initially developed a Convolutional Neural Network (CNN) based OD model to tackle this issue. This model is trained to accurately identify the presence of chairs and individuals on the production floor. The identified objects are then passed to the CNN based tracker, which tracks them throughout their life cycle in the workstation. The extracted meta-data is further processed through a novel framework for the capacity constraint analysis. We identified that the Station C is only 70.6% productive through 6 months. Additionally, the time spent at each station is recorded and aggregated for each object. This data proves helpful in conducting annual audits and effectively managing labor and material over time.

本研究采用基于深度学习的目标检测方法，开发了一个卷积神经网络模型用于智能制造中的容量约束分析，通过追踪和元数据提取，发现生产线上的某个工位的生产力仅为 70.6%，并利用该数据进行年度审计和有效管理劳动力与物料。

利用物体检测进行智能制造的容量限制分析