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- W4379797265 abstract "국내 닭고기 소비량은 지속적으로 증가하는 추세이며, 소비자들의 요구사항은 다양화되고 고도화되어 왔다. 소비자들의 요구에 따라 닭 도체에 대해 품질 등급판정을 하는데, 우수하고 안전한 닭 품질을 위해 축산물등급판정사가 ‘뼈의 상태’, ‘외관’, ‘외상’ 등을 육안으로 판정한다. 특히 ‘뼈의 상태’는 탈골 및 골절이 몇 개인지에 따라 닭 품질 등급이 달라진다. 이 판별작업은 노동집약적이기 때문에 사람으로 인한 실수가 발생할 가능성이 크고, 가공, 선별, 포장의 과정을 자동화할 때 가장 정체되는 구간이 될 수 있다. 이를 해결하기 위해 본 연구에서는 기계 시각 시스템을 활용한 닭도체의 품질판정 알고리즘을 개발하였다. 빠르게 이동하는 닭 도체의 영상을 획득하기 위해 Non-Interlace 방식의 카메라를 사용하였고, 영상에서 배경을 제거하기 위해 EM(Expectation Maximization) 알고리즘을 적용하였다. 날개 탈골 여부를 판정하기 위해 닭 도체 날개를 기하학적 형태로 분석 및 검출하는 알고리즘을 개발하였고, 이를 통해서 비접촉/비파괴 방식으로 닭 도체 품질판정 시스템을 제안하였다. 총 닭 도체 40마리 중 탈골된 닭 도체 20마리, 탈골되지 않은 닭 도체 20마리를 본 연구에서 제시한 알고리즘에 적용한 결과 판별 정확도는 100%이다. 따라서 제안한 영상처리 알고리즘이 날개 탈골 검출에 효과적임을 알 수 있다. The demand for domestic chickens has been increasing, and their quality is evaluated using factors such as bone condition, appearance, and bruising. However, the inspection process is labor-intensive and prone to human error, making it a barrier to fully automating chicken processing. In this study, an algorithm was developed using a machine vision system to automate the inspection of dislocations in chicken carcasses. The algorithm uses a non-interlace camera and an EM image-processing algorithm to separate the background and the region of interest. It analyzes the geometric shape of the wings to detect wing dislocations and determine the quality of the carcass in a non-contact and non-destructive manner. The proposed algorithm detected wing dislocations in all 40 tested samples of chicken carcasses, including 20 with wing dislocations and 20 normal ones. This study shows the potential for using machine vision systems to automate the inspection process and improve the efficiency and accuracy of chicken processing." @default.
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- W4379797265 date "2023-05-31" @default.
- W4379797265 modified "2023-10-16" @default.
- W4379797265 title "Development of Detection Algorithm for Wing Dislocation of Chicken Carcass" @default.
- W4379797265 doi "https://doi.org/10.5762/kais.2023.24.5.245" @default.
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