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Cigarette Defect Detection using Convolutional Neural Network
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초록
In factory automation, efforts are being made to increase productivity while maintaining high-quality products. In this study, a CNN network structure was designed to quickly and accurately recognize a cigarette located in the opposite direction or a cigarette with a loose end in an automated facility rotating at high speed for cigarette production. Tobacco inspection requires a simple network structure and fast processing time and performance. The proposed network has an excellent accuracy of 96.33% and a short processing time of 0.527 msec, showing excellent performance in learning time and performance compared to other CNN networks, confirming its practicality. In addition, it was confirmed that efficient learning is possible by increasing a small number of image data through a rotation conversion method.
Abstract
In factory automation, efforts are being made to increase productivity while maintaining high-quality products. In this study, a CNN network structure was designed to quickly and accurately recognize a cigarette located in the opposite direction or a cigarette with a loose end in an automated facility rotating at high speed for cigarette production. Tobacco inspection requires a simple network structure and fast processing time and performance. The proposed network has an excellent accuracy of 96.33% and a short processing time of 0.527 msec, showing excellent performance in learning time and performance compared to other CNN networks, confirming its practicality. In addition, it was confirmed that efficient learning is possible by increasing a small number of image data through a rotation conversion method.
참고문헌 (21)
- Foivos Psarommatis, "Zero-defect manufacturing the approach for higher manufacturing sustainability in the era of industry 4. 0 : a position paper", International Journal of Production Research, 60(1), pp.73~91 (2022)
- Zhonghe Ren, "State of the Art in Defect Detection Based on Machine Vision", International Journal of Precision Engineering and Manufacturing-Green Technology, 9(2), pp.661~691 (2022), 한국정밀공학회[DOI]
- A Nazar, "Quality Control of Cigarettes Packaging using Convolutional Neural Network", IOP Conference Series: Materials Science and Engineering, pp.012002~
- Wei Ying, "Detection of cigarette missing in packing based on deep convolutional neural network", 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), pp.1252~1256
- Mehmet Baygin, "Machine vision based defect detection approach using image processing", 2017 international artificial intelligence and data processing symposium (IDAP), pp.1~5
- Hyo-Keun Kim, "Global Tobacco Industry", Journal of the Korean Society of Tobacco Science, 36(1), pp.34~47 (2014)
- Lihong Zhang, "Review of Intelligent Development of Cigarette Maker’s Control System", Journal of Physics: Conference Series, pp.052042~
- Rui Qu, "Detection of cigarette appearance defects based on improved SSD model", Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, pp.1148~1153
- Jia Deng, "Imagenet: A large-scale hierarchical image database", 2009 IEEE conference on computer vision and pattern recognition, pp.248~255
- Linsheng Chen, "Prediction method of cigarette draw resistance based on correlation analysis", Computers and Electronics in Agriculture, 208, pp.107808~ (2023)
- Derek P., "Smart, LOOSE END DETECTOR FOR TOBACCO", Lorillard Tobacco Company, ~ (1998)
- Yiwei Zhang Ming Ren, "Cigarette loose-end detection method based on machine vision technology, China", CETC 41 Institute Shanghai Tobacco Machinery Co Ltd, ~ (2016)
- Yann LeCun, "Gradient-based learning applied to document recognition", Proceedings of the IEEE, 86(11), pp.2278~2324 (1998)
- Karen Simonyan, "Very deep convolutional networks for large-scale image recognition", arXiv preprint arXiv:1409. 1556, ~ (2014)
- Kaiming He, "Deep residual learning for image recognition", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.770~778
- Alex Krizhevsky, "Imagenet classification with deep convolutional neural networks", Advances in neural information processing systems, 25 (2012)
- Forrest N Iandola, "SqueezeNet : AlexNet-level accuracy with 50x fewer parameters and< 0. 5 MB model size", arXiv preprint arXiv:1602. 07360, ~ (2016)
- Mark Sandler, "Mobilenetv2: Inverted residuals and linear bottlenecks", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.4510~4520
- Xiangyu Zhang, "Shufflenet: An extremely efficient convolutional neural network for mobile devices", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.6848~6856
- Lorenzo Brigato, "A close look at deep learning with small data", 2020 25th International Conference on Pattern Recognition (ICPR), pp.2490~2497
- Christian Szegedy, "Going deeper with convolutions", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1~9