基于Transformer-CNN分割的锂金属电池质量控制
Lithium Metal Battery Quality Control via Transformer-CNN Segmentation
AI总结：
本文提出基于Transformer的TransforCNN语义分割网络，用于从XCT图像中分割锂金属电池枝晶，并在mIoU和mDSC等指标上优于U-Net、Y-Net和E-Net。
摘要：
锂金属电池（LMB）因其高理论能量密度而有望成为下一代电池系统。然而，由不均匀的锂（Li）沉积形成的被称为枝晶的缺陷阻碍了LMB的开发与利用。用于观察枝晶形貌的非破坏性技术通常使用X射线计算机断层扫描（XCT）来提供横截面视图。为了获取电池内部的三维结构，图像分割对于定量分析XCT图像变得至关重要。本工作提出了一种新的语义分割方法，使用基于Transformer的神经网络TransforCNN，能够从XCT数据中分割出枝晶。此外，我们将所提出的TransforCNN与另外三种算法进行了性能比较，包括U-Net、Y-Net和E-Net，它们构成了用于XCT分析的集成网络模型。我们的结果表明，在评估过分割指标（如平均交并比（mIoU）和平均Dice相似系数（mDSC））时，以及通过若干定性比较可视化，使用TransforCNN具有优势。
英文摘要：
Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, such as U-Net, Y-Net, and E-Net, consisting of an Ensemble Network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean Intersection over Union (mIoU) and mean Dice Similarity Coefficient (mDSC) as well as through several qualitatively comparative visualizations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
