Human assembly process recognition in human–robot collaboration (HRC) has been studied recently. However, most research works do not cover high-precision and long-timespan sub-assembly recognition. Hence this paper aims to deal with this problem.,To deal with the above-mentioned problem, the authors propose a 3D long-term recurrent convolutional networks (LRCN) by combining 3D convolutional neural networks (CNN) with long short-term memory (LSTM). 3D CNN behaves well in human action recognition. But when it comes to human sub-assembly recognition, the accuracy of 3D CNN is very low and the number of model parameters is huge, which limits its application in human sub-assembly recognition. Meanwhile, LSTM has the incomparable superiority of long-time memory and time dimensionality compression ability. Hence, by combining 3D CNN with LSTM, the new approach can greatly improve the recognition accuracy and reduce the number of model parameters.,Experiments were performed to validate the proposed method and preferable results have been obtained, where the recognition accuracy increases from 82% to 99%, recall ratio increases from 95% to 100% and the number of model parameters is reduced more than 8 times.,The authors focus on a new problem of high-precision and long-timespan sub-assembly recognition in the area of human assembly process recognition. Then, the 3D LRCN method is a new method with high-precision and long-timespan recognition ability for human sub-assembly recognition compared to 3D CNN method. It is extraordinarily valuable for the robot in HRC. It can help the robot understand what the sub-assembly human cooperator has done in HRC.

中文翻译：

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用于人机协作中人类子组件识别的 3D 长期循环卷积网络

最近研究了人机协作（HRC）中的人体装配过程识别。然而，大多数研究工作并未涵盖高精度和长时间跨度的子组件识别。因此本文旨在解决这个问题。为了解决上述问题，作者通过将 3D 卷积神经网络 (CNN) 与长短期记忆相结合，提出了一种 3D 长期循环卷积网络 (LRCN)。 LSTM）。3D CNN 在人类动作识别方面表现良好。但在人体子装配识别方面，3D CNN的准确率很低，模型参数数量庞大，限制了其在人体子装配识别中的应用。同时，LSTM在长时间记忆和时间维数压缩能力方面具有无可比拟的优越性。因此，通过将 3D CNN 与 LSTM 相结合，新方法可以大大提高识别准确率并减少模型参数的数量。,通过实验验证了所提出的方法并获得了较好的结果,其中识别准确率从82%提高到99%,召回率从95提高% 到 100%，模型参数数量减少 8 倍以上。,作者重点研究了人体装配过程识别领域高精度、长跨度子装配识别的新问题。那么，3D LRCN方法是一种较3D CNN方法具有高精度、长跨度识别能力的人体子装配识别新方法。对于 HRC 中的机器人来说，它具有非凡的价值。它可以帮助机器人了解子装配人工合作者在 HRC 中做了什么。