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http://dx.doi.org/10.46670/JSST.2021.30.5.300

Detection of Needle in trimmings or meat offals using DCGAN  

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)
Publication Information
Journal of Sensor Science and Technology / v.30, no.5, 2021 , pp. 300-308 More about this Journal
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
X-Ray image; Deep convolutional generative adversarial network (DCGAN); Needle detection; Foreign object;
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