Defect Detection in Battery Cells Using U-Net-Based Neural Networks

Defect Detection in Battery Cells Using U-Net-Based Neural Networks

Stať ve sborníku v databázi WoS či Scopus

Battery technologies are widely used in various applications, including smartphones, wearable devices, laptops, and mobile phones. In this study, we propose a methodology specifically designed for segmenting defects in 2D CT slices of battery cells. This methodology involves the creation of datasets aimed at defect detection, along with benchmarking several U-Net-based models on these datasets. Our results indicate that the U-Net+Xception model achieves the highest binary accuracy of 0.9982 and an Intersection over Union (IoU) score of 0.8066, demonstrating its strong capability to differentiate between background and foreground regions. In summary, this work establishes a valuable baseline for CT-based defect detection in battery cells, showcasing the benefits of combining U-Net architectures with advanced pre-trained encoders. This research contributes to the development of scalable and automated inspection tools that can be integrated into battery manufacturing and diagnostic processes.

Battery technologies are widely used in various applications, including smartphones, wearable devices, laptops, and mobile phones. In this study, we propose a methodology specifically designed for segmenting defects in 2D CT slices of battery cells. This methodology involves the creation of datasets aimed at defect detection, along with benchmarking several U-Net-based models on these datasets. Our results indicate that the U-Net+Xception model achieves the highest binary accuracy of 0.9982 and an Intersection over Union (IoU) score of 0.8066, demonstrating its strong capability to differentiate between background and foreground regions. In summary, this work establishes a valuable baseline for CT-based defect detection in battery cells, showcasing the benefits of combining U-Net architectures with advanced pre-trained encoders. This research contributes to the development of scalable and automated inspection tools that can be integrated into battery manufacturing and diagnostic processes.

deep learning; battery cells; defect detection; segmentation; computed tomography

deep learning; battery cells; defect detection; segmentation; computed tomography

MEZINA, A.; BURGET, R.; KOTRLY, M.

2026

03.11.2025

IEEE

Italy

979-8-3315-7675-2

2025 17th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

164

169

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