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.

翻译：基于深度学习的目标检测方法及其实际应用的日益普及，为智能制造领域开辟了新途径。受新冠肺炎疫情影响面临产能瓶颈的传统产业，亟需非侵入式方法深入分析运营流程，以实现优化并提升收益。本研究首先开发了基于卷积神经网络的目标检测模型来解决该问题。该模型经过训练可精准识别生产现场椅子与人员的存在状态，随后将所识别的目标传输至基于CNN的追踪器，实现其在工位全生命周期的动态追踪。通过新型框架对提取的元数据进行进一步处理，开展容量约束分析。研究发现，C工位在6个月内的生产效率仅为70.6%。此外，各工位的时间消耗数据被记录并汇总至每个目标对象。这些数据对于开展年度审计、长期有效管理劳动力与物料资源具有重要价值。