X-선 영상과 합성곱 신경망을 이용한 육류 내의 바늘 검출
Detection of Needles in Meat using X-Ray Images and Convolution Neural Networks
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초록
The most lethal foreign body in meat is a needle, and X-ray images are used to detect it. However, because the difference in thickness and fat content is severe depending on the type of meat and the part of the meat, the shade difference and contrast appear severe. This problem causes difficulty in automatic classification. In this paper, we propose a method for generating training patterns by efficient preprocessing and classifying needles in meat using a convolution neural network. Approximately 24000 training patterns and 4000 test patterns were used to verify the proposed method, and an accuracy of 99.8% was achieved.
Abstract
The most lethal foreign body in meat is a needle, and X-ray images are used to detect it. However, because the difference in thickness and fat content is severe depending on the type of meat and the part of the meat, the shade difference and contrast appear severe. This problem causes difficulty in automatic classification. In this paper, we propose a method for generating training patterns by efficient preprocessing and classifying needles in meat using a convolution neural network. Approximately 24000 training patterns and 4000 test patterns were used to verify the proposed method, and an accuracy of 99.8% was achieved.
참고문헌 (11)
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