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Deep learning for part identification based on inherent features
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DOI：10.1016/j.cirp.2019.04.095.png)
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摘要
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The identification of parts is essential for the efficient automation of logistic processes such as part supply in assembly and disassembly. This paper describes a new method for the optical identification of parts without explicit codes but based on inherent geometrical features with Deep Learning. The paper focusses on the improvement of training of Deep Learning systems taking into account conflicting factors such as limited training data and high variety of parts. Based on a case study in turbine industry the effects of steadily growing training data on the robustness of part classification are evaluated. (C) 2019 CIRP. Published by Elsevier Ltd. All rights reserved.
Keyword:
Object recognition
Identification
Neural network
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3.6
论文数:
3.4K
被引数:
1.3W
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