基于深度学习方法的未修剪算子标准清洗动作解析

W. Pan, S. Chou
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引用次数: 1

摘要

对于在洁净室进行的工艺,小颗粒不仅会造成环境污染,还会导致产品收率的降低。因此,在进入洁净室之前,清除体内的颗粒是很重要的。本文介绍了一种利用实时摄像机自动监控清洗动作的方法。目前的动作识别方法采用三维卷积神经网络(3DCNN)和以You Only Look Once (YOLO)为骨干的实时目标检测。为了实现未修剪的标准清理动作解析,我们的研究提出了一种将两种方法与所提出的机制相结合的新方法。除了考虑对不同动作的粗粒度分析外,本文还提出了一种细粒度的动作完成度量。
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Untrimmed Operator Standard Cleaning Action Parsing Based on Deep Learning Method
For the process in the clean room, small particles will not only cause environmental pollution, but also lead to the decrease of product yield. Therefore, it is important to clear away the particles from the body before entering the clean room. This paper described an existing approach for automated monitoring cleaning action on real-time camera. The current method of performing action recognition uses 3D convolutional neural network (3DCNN) and real-time object detection which uses You Only Look Once (YOLO) as backbone. To achieve untrimmed standard cleaning action parsing, our research proposes a new approach by combining the two methods with proposed mechanisms. In addition to considering coarse-grained analysis of different actions, this paper also proposed a fine-grained measure of action completion.
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