Enhancing Battery Exterior Defect Inspection Accuracy Through Defect-Background Separated GAN Development

This paper aims to develop a defect-background separated generative adversarial network (GAN) using deep learning and GAN to enhance the accuracy of battery exterior defect inspection. In actual battery production lines, the occurrence rates of defects vary by defect type, making it challenging to c...

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Главные авторы:	Donghun Ku, Heui Jae Pahk
Формат:	Artigo
Язык:	Inglês
Опубликовано:	IEEE 2024-01-01
Серии:	IEEE Access
Предметы:	Battery exterior defect inspection deep learning defect segmentation generative adversarial network synthetic defect
Online-ссылка:	https://ieeexplore.ieee.org/document/10477991/
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