Leveraging Foundation Model Automatic Data Augmentation Strategies and
  Skeletal Points for Hands Action Recognition in Industrial Assembly Lines
Liang Wu X. -G. Ma
On modern industrial assembly lines, many intelligent algorithms have been
developed to replace or supervise workers. However, we found that there were
bottlenecks in both training datasets and real-time performance when deploying
algorithms on actual assembly line. Therefore, we developed a promising
strategy for expanding industrial datasets, which utilized large models with
strong generalization abilities to achieve efficient, high-quality, and
large-scale dataset expansion, solving the problem of insufficient and
low-quality industrial datasets. We also applied this strategy to video action
recognition. We proposed a method of converting hand action recognition
problems into hand skeletal trajectory classification problems, which solved
the real-time performance problem of industrial algorithms. In the "hand
movements during wire insertion" scenarios on the actual assembly line, the
accuracy of hand action recognition reached 98.8\%. We conducted detailed
experimental analysis to demonstrate the effectiveness and superiority of the
method, and deployed the entire process on Midea's actual assembly line.