Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Tianyuan LiuCollege of Mechanical Engineering, Dong Hua University, Shanghai, ChinaView further author information

Jinsong BaoCollege of Mechanical Engineering, Dong Hua University, Shanghai, ChinaCorrespondence[email protected]

https://orcid.org/0000-0003-1999-1003 View further author information

Junliang WangCollege of Mechanical Engineering, Dong Hua University, Shanghai, ChinaView further author information

Jiacheng WangCollege of Mechanical Engineering, Dong Hua University, Shanghai, ChinaView further author information

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A. Papavasileiou, P. Aivaliotis, S. Aivaliotis & S. Makris. (2022) An optical system for identifying and classifying defects of metal parts. International Journal of Computer Integrated Manufacturing 35:3, pages 326-340.
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Joon-Hyung Park, Yeong-Seok Kim, Hwi Seo & Yeong-Jun Cho. (2023) Analysis of Training Deep Learning Models for PCB Defect Detection. Sensors 23:5, pages 2766.
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Kung-Jeng Wang, Hao Fan-Jiang & Ya-Xuan Lee. (2022) A multiple-stage defect detection model by convolutional neural network. Computers & Industrial Engineering 168, pages 108096.
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Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

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Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

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