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장원재 ( Won-jae Jang ),차윤석 ( Yun-seok Cha ),금예은 ( Ye-eun Keum ),이예진 ( Ye-jin Lee ),김정도 ( Jeong-do Kim ) 한국센서학회 2021 센서학회지 Vol.30 No.5
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).