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| Part Recognition Method Based on Visual Selective Attention Mechanism and Deep Learning |
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Abstract In order to enable the industrial robots to recognize the specific
targets quickly and accurately on the assembly line, an object
recognition method driven by visual selective attention mechanism is
proposed. With mass training data and a machine learning model
containing a number of hidden layers, deep learning can learn more
useful features, and thus ultimately improve the classification or
the prediction accuracy. The main idea of this method is as follows:
for all part images, the visual attention mechanism is used to
choose salient regions in an image, achieving the goal of target
segmentation. Then an image recognition method based on deep
learning is applied to recognize the chosen salient regions.
Experimental results show the effectiveness of the proposed method
and the cognitive rationality.
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| Cite this article: |
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Dan Zhou,Nanfeng Xiao. Part Recognition Method Based on Visual Selective Attention Mechanism and Deep Learning[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(4): 791-800.
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2015, Vol. 8 
