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基于双通道多模态卷积网络的电子元器件缺陷分类检测
Defect Classification in Electronic Components Using Dual-Channel Multi-Modal Convolutional Network

DOI: 10.12677/HJDM.2023.133027, PP. 269-277

Keywords: 深度学习,电子元器件缺陷分类,双通道卷积神经网络,多模态,特征融合,Deep Learning, Electronic Component Defect Classification, Dual-Channel Convolutional Neural Network, Multimodal, Feature Fusion

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Abstract:

目前在电子元器件的缺陷检测领域,主要是基于机器学习的传统图像处理算法和基于深度学习的智能图像算法。深度卷积网络可以自动提取图像更深层的特征,避免了传统图像处理算法特征提取的复杂性和盲目性。因此,本文提出一种基于双通道多模态卷积网络的电子元器件缺陷分类方法,将电子元器件影像分为Pass和Fail两个类别。首先,为提高模型的泛化能力,本文使用了Top光源和Side光源两个模态的数据。其次,为解决训练样本不足和类间样本不平衡的问题,使用PCA Jittering对数据集进行扩增。最后,为了实现模型对不同模态数据的有效覆盖,本文设计了一种基于特征融合的双通道卷积神经网络。实验表明,本文的电子元器件缺陷分类方法能够更有效地处理训练样本不足的问题,并在训练过程中通过特征融合提高了模型的性能。这将为电子信息产业的发展提供重要的技术支持和应用前景。
In the field of defect detection for electronic components, the main methods are traditional image processing algorithms based on machine learning and intelligent image algorithms based on deep learning. Deep convolutional networks can automatically extract deeper features of images, avoiding the complexity and blindness of feature extraction in traditional image processing algorithms. Therefore, this paper proposes a method for classifying electronic component defects based on a dual-channel multimodal convolutional network, dividing electronic component images into two categories: Pass and Fail. Firstly, to improve the generalization ability of the model, this paper uses data from two modalities: Top light and Side light. Secondly, to solve the problem of insufficient training samples and class imbalance between samples, PCA Jittering is used to augment the dataset. Finally, in order to achieve effective coverage of the model for different modal data, this paper designs a dual-channel convolutional neural network based on feature fusion. Experiments show that the electronic component defect classification method proposed in this paper can more effectively deal with the problem of insufficient training samples and improve the performance of the model through feature fusion during training. This will provide important technical support and application prospects for the development of the electronic information industry.

References

[1]  Ren, Z., Fang, F., Yan, N. and Wu, Y. (2022) State of the Art in Defect Detection Based on Machine Vision. Interna-tional Journal of Precision Engineering and Manufacturing-Green Technology, 9, 661-691.
https://doi.org/10.1007/s40684-021-00343-6
[2]  Malamas, E.N., Petrakis, E.G.M., Zervakis, M., Petit, L. and Legat, J.D. (2003) A Survey on Industrial Vision Systems, Applications and Tools. Image and Vision Computing, 21, 171-188.
https://doi.org/10.1016/S0262-8856(02)00152-X
[3]  Davies, E.R. (2012) Computer and Machine Vi-sion: Theory, Algorithms, Practicalities. Academic Press, Cambridge.
[4]  Park, J.K., Kwon, B.K., Park, J.H. and Kang D.J., (2016) Machine Learning-Based Imaging System for Surface Defect Inspection. International Journal of Precision Engineering and Manufacturing-Green Technology, 3, 303-310.
https://doi.org/10.1007/s40684-016-0039-x
[5]  Wang, T., Chen, Y., Qiao, M. and Snoussi, H. (2018) A Fast and Robust Convolutional Neural Network-Based Defect Detection Model in Product Quality Control. The International Journal of Advanced Manufacturing Technology, 94, 3465-3471.
https://doi.org/10.1007/s00170-017-0882-0
[6]  Liao, Z., Abdelhafeez, A., Li, H., et al. (2019) State-of-the-Art of Surface Integrity in Machining of Metal Matrix Composites. International Journal of Machine Tools and Manufacture, 143, 63-91.
https://doi.org/10.1016/j.ijmachtools.2019.05.006
[7]  Capizzi, G., Sciuto, G.L., Napoli, C., et al. (2015) Automatic Classification of Fruit Defects Based on Co-Occurrence Matrix and Neural Networks. 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lódz, 13-16 September 2015, 861-867.
[8]  Li, X., Jiang, H. and Yin, G. (2014) Detection of Surface Crack Defects on Ferrite Magnetic Tile. NDT & E International, 62, 6-13.
https://doi.org/10.1016/j.ndteint.2013.10.006
[9]  Chen, J. and Jain, A.K. (1988) A Structural Approach to Identify Defects in Textured Images. Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics, Beijing, 8-12 August 1988, 29-32.
[10]  Bennamoun, M. and Bodnarova, A. (1998) Automatic Visual Inspection and Flaw Detection in Textile Materials: Past, Present and Future. SMC’98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), San Diego, 14 October 1998, 4340-4343.
[11]  Lin, H., Li, B., Wang, X.G., Shu, Y.F. and Niu, S.L. (2019) Automated Defect Inspection of LED Chip Using Deep Convolutional Neural Network. Journal of Intelligent Manufacturing, 30, 2525-2534.
https://doi.org/10.1007/s10845-018-1415-x
[12]  Kumar, S.S., Abraham, D.M., Jahanshahi, M.R., Iseley, T. and Starr, J. (2018) Automated Defect Classification in Sewer Closed Circuit Television Inspections Using Deep Convolu-tional Neural Networks. Automation in Construction, 91, 273-283.
https://doi.org/10.1016/j.autcon.2018.03.028
[13]  Wang, Y., Liu, M., Zheng, P., Yang, H.Y. and Zou, J. (2020) A Smart Surface Inspection System Using Faster R-CNN in Cloud-Edge Computing Environment. Advanced Engineering Informatics, 43, Article ID: 101037.
https://doi.org/10.1016/j.aei.2020.101037
[14]  O’Shea, K. and Nash, R. (2015) An Introduction to Convolutional Neural Networks. arXiv: 1511.08458.
[15]  He, K.M., Zhang, X.Y., Ren, S.Q. and Sun, J. (2016) Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 27-30 June 2016, 770-778.
https://doi.org/10.1109/CVPR.2016.90

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