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http://dx.doi.org/10.6109/jkiice.2022.26.11.1720

Sparse Class Processing Strategy in Image-based Livestock Defect Detection  

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)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.11, 2022 , pp. 1720-1728 More about this Journal
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.
Keywords
Smart farm; livestock operations; Artificial intelligence; Imbalanced data; Anomaly detection;
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