Real-time defect detection network for polarizer based on deep learning
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DOI: 10.1007/s10845-020-01536-7
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References listed on IDEAS
- Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
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- Yuanyuan Wang & Ling Ma & Lihua Jian & Huiqin Jiang, 2023. "Conductive particle detection via efficient encoder–decoder network," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3563-3577, December.
- Zhuxi Ma & Yibo Li & Minghui Huang & Qianbin Huang & Jie Cheng & Si Tang, 2023. "Automated real-time detection of surface defects in manufacturing processes of aluminum alloy strip using a lightweight network architecture," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2431-2447, June.
- Danqing Kang & Jianhuang Lai & Junyong Zhu & Yu Han, 2023. "An adaptive feature reconstruction network for the precise segmentation of surface defects on printed circuit boards," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3197-3214, October.
- Xinyu Suo & Jian Liu & Licheng Dong & Chen Shengfeng & Lu Enhui & Chen Ning, 2022. "A machine vision-based defect detection system for nuclear-fuel rod groove," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1649-1663, August.
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Keywords
Automatic testing; Image classification; Defect detection; Global average pooling; Parallel module;All these keywords.
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