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Piezoresistor defect classification using convolutional neural networks based on incremental branch growth

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Abstract

Surface defects significantly deteriorate piezoresistor quality. We propose an accurate end-to-end classification method for piezoresistor surface defects based on an incremental branch growth convolutional neural network (IBG-CNN) for automatic CNN construction. First, an incremental branch (IB) is proposed to grow a CNN dynamically. Then, IBG-CNN establishes and trains a starting network based on IB, which is then grown continuously using the IBG algorithm. The next generation of the growing network is trained using the pretrained previous generation, which significantly accelerates the search process of the network model. A CNN based on IBG-CNN for classifying six piezoresistor defect types was automatically built on one GPU in only approximately 16 h. A test dataset of 6248 images was evaluated using the mean average precision (mAP). The experimental results demonstrate that the classification accuracy of our algorithm (mAP = 0.935) is higher than or very close to those of state-of-the-art methods, i.e., conventional CNN-based methods and the efficient neural architecture search (ENAS).

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Acknowledgments

We would like to thank NVIDIA for providing the Titan X GPU used in this study.

Funding

This study was partially supported by the National Natural Science Foundation of China (62166012, 61941202) and the Guangxi Natural Science Foundation (2018GXNSFBA281081).

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, Shanxi, China

    Lin Huang & Yi-Gong Zhao

  2. Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin, Guangxi, China

    Lin Huang & Tie-Jun Yang

Authors
  1. Lin Huang
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  2. Yi-Gong Zhao
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  3. Tie-Jun Yang
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Contributions

Methodology, L.H. and T.-J.Y.; writing (original draft preparation), L.H.; supervision, Y.-G.Z.; funding acquisition, L.H.

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Correspondence to Yi-Gong Zhao.

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Huang, L., Zhao, YG. & Yang, TJ. Piezoresistor defect classification using convolutional neural networks based on incremental branch growth. Multimed Tools Appl 81, 16743–16760 (2022). https://doi.org/10.1007/s11042-022-12651-3

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  • DOI: https://doi.org/10.1007/s11042-022-12651-3

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