Real-time assembly operation recognition with fog computing and transfer learning for human-centered intelligent manufacturing
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In a human-centered intelligent manufacturing system, every element is to assist the operator in achieving the optimal operational performance. The primary task of developing such a human-centered system is to accurately understand human behavior. In this paper, we propose a fog computing framework for assembly operation recognition, which brings computing power close to the data source in order to achieve real-time recognition. For data collection, the operator's activity is captured using visual cameras from different perspectives. For operation recognition, instead of directly building and training a deep learning model from scratch, which needs a huge amount of data, transfer learning is applied to transfer the learning abilities to our application. A worker assembly operation dataset is established, which at present contains 10 sequential operations in an assembly task of installing a desktop CNC machine. The developed transfer learning model is evaluated on this dataset and achieves a recognition accuracy of 95% in the testing experiments.
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