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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Detalles Bibliográficos

Autores principales:	Donghun Ku, Heui Jae Pahk
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	IEEE 2024-01-01
Colección:	IEEE Access
Materias:	Battery exterior defect inspection deep learning defect segmentation generative adversarial network synthetic defect
Acceso en línea:	https://ieeexplore.ieee.org/document/10477991/
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