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논문 기본 정보

자료유형
학술저널
저자정보
MinJu Kim (The University of Queensland) YoHan Choi (Rural Development Administration) 이정남 (Kangwon National University) Soo-Jin Sa (RDA) Hyun-chong Cho (Kangwon National University Korea)
저널정보
한국축산학회(구 한국동물자원과학회) 한국축산학회지 한국축산학회지 제63권 제6호
발행연도
2021.11
수록면
1,453 - 1,463 (11page)
DOI
10.5187/jast.2021.e127

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초록· 키워드

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Feeding is the most important behavior that represents the health and welfare of weanling pigs. The early detection of feed refusal is crucial for the control of disease in the initial stages and the detection of empty feeders for adding feed in a timely manner. This paper proposes a real-time technique for the detection and recognition of small pigs using a deep-leaningbased method. The proposed model focuses on detecting pigs on a feeder in a feeding position. Conventional methods detect pigs and then classify them into different behavior gestures. In contrast, in the proposed method, these two tasks are combined into a single process to detect only feeding behavior to increase the speed of detection. Considering the significant differences between pig behaviors at different sizes, adaptive adjustments are introduced into a you-only-look-once (YOLO) model, including an angle optimization strategy between the head and body for detecting a head in a feeder. According to experimental results, this method can detect the feeding behavior of pigs and screen non-feeding positions with 95.66%, 94.22%, and 96.56% average precision (AP) at an intersection over union (IoU) threshold of 0.5 for YOLOv3, YOLOv4, and an additional layer and with the proposed activation function, respectively. Drinking behavior was detected with 86.86%, 89.16%, and 86.41% AP at a 0.5 IoU threshold for YOLOv3, YOLOv4, and the proposed activation function, respectively. In terms of detection and classification, the results of our study demonstrate that the proposed method yields higher precision and recall compared to conventional methods.

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