Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Sparse Class Processing Strategy in Image-based Livestock Defect Detection

이미지 기반 축산물 불량 탐지에서의 희소 클래스 처리 전략

  • Lee, Bumho (Graduate School of Data Science, Department of Industrial and System Engineering, KAIST) ;
  • Cho, Yesung (Graduate School of Data Science, Department of Industrial and System Engineering, KAIST) ;
  • Yi, Mun Yong (Department of Industrial and Systems Engineering, KAIST)
  • Received : 2022.10.18
  • Accepted : 2022.10.30
  • Published : 2022.11.30
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Abstract

The industrial 4.0 era has been opened with the development of artificial intelligence technology, and the realization of smart farms incorporating ICT technology is receiving great attention in the livestock industry. Among them, the quality management technology of livestock products and livestock operations incorporating computer vision-based artificial intelligence technology represent key technologies. However, the insufficient number of livestock image data for artificial intelligence model training and the severely unbalanced ratio of labels for recognizing a specific defective state are major obstacles to the related research and technology development. To overcome these problems, in this study, combining oversampling and adversarial case generation techniques is proposed as a method necessary to effectively utilizing small data labels for successful defect detection. In addition, experiments comparing performance and time cost of the applicable techniques were conducted. Through experiments, we confirm the validity of the proposed methods and draw utilization strategies from the study results.

인공지능 기술의 발전으로 산업 4.0시대가 열렸고 축산업에서도 ICT 기술이 접목된 스마트 농장의 구현이 큰 관심을 받고 있다. 그중에서도 컴퓨터 비전 기반 인공지능 기술을 접목한 축산물 및 축산 가공품의 품질 관리 기술은 스마트 축산의 핵심 기술에 해당한다. 그러나 인공지능 모형 훈련을 위한 축산물 이미지 데이터 수의 부족과 특정 범주(class)에 대한 데이터 불균형은 관련 연구 및 기술 개발에 큰 장해물이 되고 있다. 이러한 문제들을 해결하기 위해, 본 연구에서는 오버샘플링과 적대적 사례 생성기법의 활용을 제안한다. 제안되는 방법은 성공적인 불량 탐지 (Defect detection) 관점을 기반으로 하며, 이는 부족한 데이터 레이블을 효과적으로 활용하는데 필요한 방법이다. 최종적으로 실험을 통해 제안된 방법의 타당성을 확인하고 활용 전략을 검토한다.

Keywords

Acknowledgement

This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) and Korea Smart Farm RD Foundation (KosFarm) through Smart Farm Innovation Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) and Ministry of Science and ICT (MSIT), Rural Development Administration (RDA) (421043-04)

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