Journal of Animal Science and Technology

  • 제66권4호
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  • Pages.846-858
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  • 2024
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  • 2672-0191(pISSN)
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  • 2055-0391(eISSN)
한국축산학회 (Korean Society of Animal Sciences and Technology)
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A study of duck detection using deep neural network based on RetinaNet model in smart farming

  • Jeyoung Lee (Department of Digital Media, The Catholic University of Korea) ;
  • Hochul Kang (Department of Digital Media, The Catholic University of Korea)
  • 투고 : 2023.06.27
  • 심사 : 2023.07.24
  • 발행 : 2024.07.31
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초록

In a duck cage, ducks are placed in various states. In particular, if a duck is overturned and falls or dies, it will adversely affect the growing environment. In order to prevent the foregoing, it was necessary to continuously manage the cage for duck growth. This study proposes a method using an object detection algorithm to improve the foregoing. Object detection refers to the work to perform classification and localization of all objects present in the image when an input image is given. To use an object detection algorithm in a duck cage, data to be used for learning should be made and the data should be augmented to secure enough data to learn from. In addition, the time required for object detection and the accuracy of object detection are important. The study collected, processed, and augmented image data for a total of two years in 2021 and 2022 from the duck cage. Based on the objects that must be detected, the data collected as such were divided at a ratio of 9 : 1, and learning and verification were performed. The final results were visually confirmed using images different from the images used for learning. The proposed method is expected to be used for minimizing human resources in the growing process in duck cages and making the duck cages into smart farms.

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과제정보

This work was supported by The Catholic University of Korea, Research Fund, 2021.

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