Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Detection of Needle in trimmings or meat offals using DCGAN

DCGAN을 이용한 잡육에서의 바늘 검출

  • Jang, Won-Jae (Division of Electronics and Display Engineering, Hoseo Unversity) Hoseo University) ;
  • Cha, Yun-Seok (Division of Electronics and Display Engineering, Hoseo Unversity) Hoseo University) ;
  • Keum, Ye-Eun (Division of Electronics and Display Engineering, Hoseo Unversity) Hoseo University) ;
  • Lee, Ye-Jin (Division of Electronics and Display Engineering, Hoseo Unversity) Hoseo University) ;
  • Kim, Jeong-Do (Division of Electronics and Display Engineering, Hoseo Unversity) Hoseo University)
  • 장원재 (호서대학교 전자디스플레이공학부) ;
  • 차윤석 (호서대학교 전자디스플레이공학부) ;
  • 금예은 (호서대학교 전자디스플레이공학부) ;
  • 이예진 (호서대학교 전자디스플레이공학부) ;
  • 김정도 (호서대학교 전자디스플레이공학부)
  • Received : 2021.08.09
  • Accepted : 2021.09.03
  • Published : 2021.09.30
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Abstract

Usually, during slaughter, the meat is divided into large chunks by part after deboning. The meat chunks are inspected for the presence of needles with an X-ray scanner. Although needles in the meat chunks are easily detectable, they can also be found in trimmings and meat offals, where meat skins, fat chunks, and pieces of meat from different parts get agglomerated. Detection of needles in trimmings and meat offals becomes challenging because of many needle-like patterns that are detected by the X-ray scanner. This problem can be solved by learning the trimmings or meat offals using deep learning. However, it is not easy to collect a large number of learning patterns in trimmings or meat offals. In this study, we demonstrate the use of deep convolutional generative adversarial network (DCGAN) to create fake images of trimmings or meat offals and train them using a convolution neural network (CNN).

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터지원사업의 연구결과로 수행되었다 (IITP-2021-2018-0-01417).

References

  1. M. R. Ahmed, J. Yasmin, W. H. Lee, C. Mo, and B. K. Cho, "Imaging technologies for nondestructive measurement of internal properties of agricultural products: a review", J. Biosyst. Eng., Vol. 42, No. 3, pp. 199-216, 2017. https://doi.org/10.5307/JBE.2017.42.3.199
  2. L. R. Correia, G. S. Mittal, and O. A. Basir, "Ultrasonic detection of bone fragment in mechanically deboned chicken breasts", Innov. Food Sci. Emerg. Technol., Vol. 9, No. 1, pp. 109-115, 2008. https://doi.org/10.1016/j.ifset.2007.06.004
  3. R. Diaz, L. Cervera, S. Fenollosa, C. Avila, and J. Belenguer, "Hyperspectral system for the detection of foreign bodies in meat products", Procedia Eng., Vol. 25, pp. 313-316, 2011. https://doi.org/10.1016/j.proeng.2011.12.077
  4. C. A. Roobottom and M. J. Weston, "The detection of foreign bodies in soft tissue-Comparison of conventional and digital radiography", Clin. Radiol., Vol. 49, No. 5, pp. 330-332, 1994. https://doi.org/10.1016/S0009-9260(05)81799-4
  5. S. Shevchik, T. Le-Quang, B. Meylan, F. V. Farahani, M. P. Olbinado, A. Rack, G. Masinelli, C. Leinenbach, and K. Wasmer, "Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance", Sci. Rep., Vol. 10, No. 1, pp. 1-12, 2020. https://doi.org/10.1038/s41598-019-56847-4
  6. M. Schultheiss, S. A. Schober, M. Lodde, J. Bodden, J. Aichele, C. Muller-Leisse, B. Renger, F. Pfeiffer, and D. Pfeiffer, "A robust convolutional neural network for lung nodule detection in the presence of foreign bodies", Sci. Rep., Vol. 10, No. 1, pp. 1-9, 2020. https://doi.org/10.1038/s41598-019-56847-4
  7. L. Zhou, C. Zhang, F. Liu, Z. Qiu, and Y. He, "Application of deep learning in food: a review", Compr. Rev. Food Sci. Food Saf., Vol. 18, No. 6, pp. 1793-1811, 2019. https://doi.org/10.1111/1541-4337.12492
  8. D. Rong, L. Xie, and Y. Ying, "Computer vision detection of foreign objects in walnuts using deep learning", Comput. Electron. Agric., Vol. 162, pp. 1001-1010, 2019. https://doi.org/10.1016/j.compag.2019.05.019
  9. K. Kim, H. Kim, J. Chun, M. Kang, M. Hong, and B. Min, "Real-time anomaly detection in packaged food X-ray images using supervised learning", Comput. Mater. Contin., Vol. 67, No. 2, pp. 2547-2568, 2021. https://doi.org/10.32604/cmc.2021.014642
  10. J. H. Ahn, W. J. Jang, W. H. Lee, and J. Do Kim, "Detection of needles in meat using X-ray images and convolution neural networks", J. Sens. Sci. Technol., Vol. 29, No. 6, pp. 427-432, 2020. https://doi.org/10.46670/JSST.2020.29.6.427
  11. J. Liu, C. Wang, H. Su, B. Du, and D. Tao, "Multistage GAN for fabric defect detection", IEEE Trans. Image Process., Vol. 29, pp. 3388-3400, 2020. https://doi.org/10.1109/TIP.2019.2959741
  12. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets", Adv. Neural Inf. Process. Syst., Vol. 27, pp. 1-9, 2014.