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27 September 2024 A novel industrial foreign impurity detection system design via deep space-spectrum fusion network and compressed sensing
Zhenjun Huang, Kaige Liang, Fan Yin, Qingpeng Li, Bowen Xie, Dongqin Yuan
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Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132811D (2024) https://doi.org/10.1117/12.3050802
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China
Abstract
To address the challenge of foreign impurity (namely NTRM, non-tobacco related material) detection in the industrial tobacco production line, such as the difficulty of manual identification of NTRM and visual fatigue, we design an online foreign impurity detection system, which is mainly based on compressed sensing, combined with deep space-spectrum fusion network, utilizing CNN-based deep learning algorithms for real-time online detection of NTRM in tobacco leaves. And then we provide the detection results and NTRM location information to workers via a human-machine interface, to assist in the manual NTRM removal process. Experimental results show that for the selected NTRM targets in this study, the average detection accuracy is as high as 85.4%, with a missed detection rate as low as 5.5%, demonstrating effective assistance in manual NTRM removal.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zhenjun Huang, Kaige Liang, Fan Yin, Qingpeng Li, Bowen Xie, and Dongqin Yuan "A novel industrial foreign impurity detection system design via deep space-spectrum fusion network and compressed sensing", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132811D (27 September 2024); https://doi.org/10.1117/12.3050802
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KEYWORDS
Computing systems
Cameras
Imaging systems
Design
Human-machine interfaces
Light sources
Lamps
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