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@article{ART003009709},
author={YuLim Kim and Jaeil Kim},
title={Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2023},
volume={28},
number={10},
pages={27-35},
doi={10.9708/jksci.2023.28.10.027}

TY - JOUR
AU - YuLim Kim
AU - Jaeil Kim
TI - Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment
JO - Journal of The Korea Society of Computer and Information
PY - 2023
VL - 28
IS - 10
PB - The Korean Society Of Computer And Information
SP - 27
EP - 35
SN - 1598-849X
AB - In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.
KW - Deep learning;CNN;Multi Class;Image classification;Defect inspection;Textile
DO - 10.9708/jksci.2023.28.10.027
ER -

YuLim Kim and Jaeil Kim. (2023). Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment. Journal of The Korea Society of Computer and Information, 28(10), 27-35.

YuLim Kim and Jaeil Kim. 2023, "Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment", Journal of The Korea Society of Computer and Information, vol.28, no.10 pp.27-35. Available from: doi:10.9708/jksci.2023.28.10.027

YuLim Kim, Jaeil Kim "Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment" Journal of The Korea Society of Computer and Information 28.10 pp.27-35 (2023) : 27.

YuLim Kim, Jaeil Kim. Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment. 2023; 28(10), 27-35. Available from: doi:10.9708/jksci.2023.28.10.027

YuLim Kim and Jaeil Kim. "Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment" Journal of The Korea Society of Computer and Information 28, no.10 (2023) : 27-35.doi: 10.9708/jksci.2023.28.10.027

YuLim Kim; Jaeil Kim. Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment. Journal of The Korea Society of Computer and Information, 28(10), 27-35. doi: 10.9708/jksci.2023.28.10.027

YuLim Kim; Jaeil Kim. Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment. Journal of The Korea Society of Computer and Information. 2023; 28(10) 27-35. doi: 10.9708/jksci.2023.28.10.027

YuLim Kim, Jaeil Kim. Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment. 2023; 28(10), 27-35. Available from: doi:10.9708/jksci.2023.28.10.027

YuLim Kim and Jaeil Kim. "Establishment of a deep learning-based defect classification system for optimizing textile manufacturing equipment" Journal of The Korea Society of Computer and Information 28, no.10 (2023) : 27-35.doi: 10.9708/jksci.2023.28.10.027