KJCKorea
Journal Central

@article{ART002834348},
author={Yonghun Jang and Myung-Sub Lee},
title={A technique for predicting the cutting points of fish for the target weight using AI machine vision},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={4},
pages={27-36},
doi={10.9708/jksci.2022.27.04.027}

TY - JOUR
AU - Yonghun Jang
AU - Myung-Sub Lee
TI - A technique for predicting the cutting points of fish for the target weight using AI machine vision
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 4
PB - The Korean Society Of Computer And Information
SP - 27
EP - 36
SN - 1598-849X
AB - In this paper, to improve the conditions of the fish processing site, we propose a method to predict the cutting point of fish according to the target weight using AI machine vision. The proposed method performs image-based preprocessing by first photographing the top and front views of the input fish. Then, RANSAC(RANdom SAmple Consensus) is used to extract the fish contour line, and then 3D external information of the fish is obtained using 3D modeling. Next, machine learning is performed on the extracted three-dimensional feature information and measured weight information to generate a neural network model. Subsequently, the fish is cut at the cutting point predicted by the proposed technique, and then the weight of the cut piece is measured. We compared the measured weight with the target weight and evaluated the performance using evaluation methods such as MAE(Mean Absolute Error) and MRE(Mean Relative Error). The obtained results indicate that an average error rate of less than 3% was achieved in comparison to the target weight. The proposed technique is expected to contribute greatly to the development of the fishery industry in the future by being linked to the automation system.
KW - AI machine vision;Image processing technology;Random sample consensus partitioning technique;Cutting point;Neural network model
DO - 10.9708/jksci.2022.27.04.027
ER -

Yonghun Jang and Myung-Sub Lee. (2022). A technique for predicting the cutting points of fish for the target weight using AI machine vision. Journal of The Korea Society of Computer and Information, 27(4), 27-36.

Yonghun Jang and Myung-Sub Lee. 2022, "A technique for predicting the cutting points of fish for the target weight using AI machine vision", Journal of The Korea Society of Computer and Information, vol.27, no.4 pp.27-36. Available from: doi:10.9708/jksci.2022.27.04.027

Yonghun Jang, Myung-Sub Lee "A technique for predicting the cutting points of fish for the target weight using AI machine vision" Journal of The Korea Society of Computer and Information 27.4 pp.27-36 (2022) : 27.

Yonghun Jang, Myung-Sub Lee. A technique for predicting the cutting points of fish for the target weight using AI machine vision. 2022; 27(4), 27-36. Available from: doi:10.9708/jksci.2022.27.04.027

Yonghun Jang and Myung-Sub Lee. "A technique for predicting the cutting points of fish for the target weight using AI machine vision" Journal of The Korea Society of Computer and Information 27, no.4 (2022) : 27-36.doi: 10.9708/jksci.2022.27.04.027

Yonghun Jang; Myung-Sub Lee. A technique for predicting the cutting points of fish for the target weight using AI machine vision. Journal of The Korea Society of Computer and Information, 27(4), 27-36. doi: 10.9708/jksci.2022.27.04.027

Yonghun Jang; Myung-Sub Lee. A technique for predicting the cutting points of fish for the target weight using AI machine vision. Journal of The Korea Society of Computer and Information. 2022; 27(4) 27-36. doi: 10.9708/jksci.2022.27.04.027

Yonghun Jang, Myung-Sub Lee. A technique for predicting the cutting points of fish for the target weight using AI machine vision. 2022; 27(4), 27-36. Available from: doi:10.9708/jksci.2022.27.04.027

Yonghun Jang and Myung-Sub Lee. "A technique for predicting the cutting points of fish for the target weight using AI machine vision" Journal of The Korea Society of Computer and Information 27, no.4 (2022) : 27-36.doi: 10.9708/jksci.2022.27.04.027