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http://dx.doi.org/10.13088/jiis.2022.28.2.263

A Deep Learning Method for Cost-Effective Feed Weight Prediction of Automatic Feeder for Companion Animals  

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)
Publication Information
Journal of Intelligence and Information Systems / v.28, no.2, 2022 , pp. 263-278 More about this Journal
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.
Keywords
pet feeder; computer vision; weight prediction; deep learning; convolutional neural network;
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