Research on Multi-side Joint Industrial Parts Inspection Based on Model Fusion

References

• Cao, L., and X. Wei. 2020. Application of Convolutional Neural Network Based on Transfer Learning for Garbage Classification.2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, Chongqing, China: 1032–2666. doi:10.1109/ITOEC49072.2020.9141699.
   Google Scholar
• Chung, F. J. K., C. Y. Shih, and J. Y. Lee. 2008. Separating color and identifying repeat pattern through the automatic computerized analysis system for printed fabrics. Journal of Information Science and Engineering 24 (2):453–67.
   Web of Science ®Google Scholar
• Demizio, D. J., and E. J. Bernstein. 2019. Detection and classification of systemic sclerosis-related interstitial lung disease: A review. Current Opinion in Rheumatology 31. doi:10.1097/BOR.0000000000000660.
   Web of Science ®Google Scholar
• Elsisi, M., T. Minh-Quang, M. Karar, L. Matti, and M. M. F. Darwish. 2021. Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings. Sensors 21 (4):1038. doi:10.3390/s21041038.
   PubMed Web of Science ®Google Scholar
• Fu, P., J. Wang, X. Zhang, L. Zhang, and R. X. Gao. 2020. Dynamic routing-based multimodal neural network for multi-sensory fault diagnosis of induction motor. Journal of Manufacturing Systems 55 (4):264–72. doi:10.1016/j.jmsy.2020.04.009.
   Google Scholar
• Huang, G., Z. Liu, and V. Laurens. 2017. Densely Connected Convolutional Networks. Computer Vision and Pattern Recognition (CVPR) 2261–69. doi:10.1109/CVPR.2017.243.
   Google Scholar
• Ioffe, S., and C. Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. International conference on machine learning,PMLR, Lille, France, Volume 37 ( ICML’15): JMLR.org, 448–56.
   Google Scholar
• Kaiming, H., Z. Xiangyu, R. Shaoqing, and S. Jian. 2016. Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), LAS VEGAS pp. 770–78. doi: 10.1109/CVPR.2016.90
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 1097-1105, 84–90. doi:10.1145/3065386.
   Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradientbased learning applied to document recognition. Proceedings of the IEEE 86 (11):2278–324. doi:10.1109/5.726791.
   Web of Science ®Google Scholar
• Li, P., Z. Tan, L. Yan, and K. Deng. 2011. Time series prediction of mining subsidence based on a SVM. Mining Science and Technology Pages:557–62. doi:10.1016/j.mstc.2011.02.025.
   Google Scholar
• Li, X., W. Zhang, and X. Bian. 2004. Research on lane marking detection based on machine vision. Journal of Southeast University 20 (2):176–80. doi:10.1007/978-3-642-25185-6_69.
   Google Scholar
• Meriama, M., and B. Mohamed. 2018. Automatic adaptation of SIFT for robust facial recognition in uncontrolled lighting conditions. IET Computer Vision 12 (5):623–33. doi:10.1049/iet-cvi.2017.0190.
   Web of Science ®Google Scholar
• Roy, S., M. Willi, O. Sebastiaan, L. Ben, F. Enrico, S. Cristiano, H. Iris, C. Nishith, M. Federico, S. Alessandro, et al. 2020. Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound. IEEE Transactions on Medical Imaging. doi: 10.1109/TMI.2020.2994459.
   Google Scholar
• Russakovsky, O., J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. 2015. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (3):211–52. doi:10.1007/s11263-015-0816-y.
   Web of Science ®Google Scholar
• Sener, B., G. Ugur, O. Murat, and U. Hakki Ozgur. 2021. A novel chatter detection method for milling using deep convolution neural networks. Measurement 182:109689. doi:10.1016/j.measurement.2021.109689.
   Web of Science ®Google Scholar
• Shah, M., L. Ana, and R. Jens. 2020. Automated classification of normal and Stargardt disease optical coherence tomography images using deep learning. Acta Ophthalmologica 98 (6). doi: 10.1111/aos.14353.
   Web of Science ®Google Scholar
• Szegedy, C., S. Ioffe, V. Vanhoucke, and A. Alemi. 2017. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Thirty-first AAAI conference on artificial intelligence, San Francisco, California USA.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, and A. Rabinovich. 2015. Going Deeper with Convolutions. Computer Vision and Pattern Recognition (CVPR) 1–9. doi:10.1109/CVPR.2015.7298594.
   Google Scholar
• Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. Rethinking the Inception Architecture for Computer Vision. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), LAS VEGAS, IEEE:2818–26. doi: 10.1109/CVPR.2016.308
   Google Scholar
• Xiaojun, B., and W. Haibo. 2019. Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning. Neural Networks. Neural Networks 114. doi:10.1016/j.neunet.2019.02.005.
   Google Scholar
• Xie, Y., T. Niu, S. Shao, Y. Zhao, and Y. Cheng. 2020. Attention-based Convolutional Neural Networks for Diesel Fuel System Fault Diagnosis.2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), Xi'an, China. 210–14. doi: 10.1109/ICSMD50554.2020.9261700.
   Google Scholar