Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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A Deep Learning Method for Cost-Effective Feed Weight Prediction of Automatic Feeder for Companion Animals

반려동물용 자동 사료급식기의 비용효율적 사료 중량 예측을 위한 딥러닝 방법

  • Kim, Hoejung (Department of Big Data Analytics, Kyung Hee University) ;
  • Jeon, Yejin (School of Management, Kyung Hee University) ;
  • Yi, Seunghyun (School of Management, Kyung Hee University) ;
  • Kwon, Ohbyung (School of Management, Kyung Hee University)
  • 김회정 (경희대학교 빅데이터응용학과) ;
  • 전예진 (경희대학교 경영학과) ;
  • 이승현 (경희대학교 경영학과) ;
  • 권오병 (경희대학교 경영학과)
  • Received : 2022.03.07
  • Accepted : 2022.03.23
  • Published : 2022.06.30
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Abstract

With the recent advent of IoT technology, automatic pet feeders are being distributed so that owners can feed their companion animals while they are out. However, due to behaviors of pets, the method of measuring weight, which is important in automatic feeding, can be easily damaged and broken when using the scale. The 3D camera method has disadvantages due to its cost, and the 2D camera method has relatively poor accuracy when compared to 3D camera method. Hence, the purpose of this study is to propose a deep learning approach that can accurately estimate weight while simply using a 2D camera. For this, various convolutional neural networks were used, and among them, the ResNet101-based model showed the best performance: an average absolute error of 3.06 grams and an average absolute ratio error of 3.40%, which could be used commercially in terms of technical and financial viability. The result of this study can be useful for the practitioners to predict the weight of a standardized object such as feed only through an easy 2D image.

최근 IoT 기술의 발달로 외출 중에도 반려동물에 급여하도록 자동 사료급식기가 유통되고 있다. 그러나 자동급식에서 중요한 중량을 측정하는 저울 방식은 쉽게 고장이 나고, 3D카메라 방식은 비용이 든다는 단점이 있으며, 2D카메라 방식은 중량 측정의 정확도가 떨어진다. 특히 사료가 복합된 경우 중량 측정 문제는 더욱 어려워질 수 있다. 따라서 본 연구의 목적은 2D카메라를 사용하면서도 중량을 정확하게 추정할 수 있는 딥러닝 접근법을 제안하는 것이다. 이를 위해 다양한 합성곱 신경망을 이용하였으며, 그중 ResNet101 기반 모델이 3.06 gram의 평균 절대 오차와 3.40%의 평균 절대비 오차를 기록하며 가장 우수한 성능을 보였다. 본 연구의 결과로 사료와 같이 규격화된 물체의 중량을 확보가 용이한 2D 이미지를 통해서만 예측할 필요가 있을 경우 유용한 정보로 활용될 수 있다.

Keywords

Acknowledgement

이 논문은 2022년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구임(No.2022-0-00904, 다품종 소량 비건육 사료 생산 스마트팩토리 기반 구축 및 비대면 큐레이팅 중개 시스템 구축 개발). 본 논문은 연구재단 4단계 BK21 사업으로부터 지원받은 연구임.

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