Acaro Chacón Ximena Carolina, Lo Scudo Fabrizio, Cappuccino Gregorio, Dodaro Carmine
University of Calabria, Arcavacata, Italy.
Sci Data. 2025 May 22;12(1):843. doi: 10.1038/s41597-025-05211-5.
In recent years, deep learning techniques have been extensively used for the identification and classification of lithium-ion batteries. However, these models typically require a costly and labor-intensive labeling process, often influenced by commercial or proprietary concerns. In this study, we introduce RecyBat24, a publicly accessible image dataset for the detection and classification of three battery types: Pouch, Prismatic, and Cylindrical. Our dataset is designed to support both academic research and industrial applications, closely replicating real-world scenarios during the acquisition process and employing data augmentation techniques to simulate various external conditions. Additionally, we demonstrate how the RecyBat24's detection-oriented annotations can be used to create a second version of RecyBat24for instance-segmentation tasks. Finally, we demonstrate that recent lightweight machine learning models achieve high accuracy, highlighting their potential for classification and segmentation applications where computational resources are constrained.
近年来，深度学习技术已被广泛用于锂离子电池的识别和分类。然而，这些模型通常需要一个成本高昂且劳动密集的标注过程，这往往受到商业或专有问题的影响。在本研究中，我们引入了RecyBat24，这是一个可公开获取的图像数据集，用于检测和分类三种电池类型：软包电池、方形电池和圆柱形电池。我们的数据集旨在支持学术研究和工业应用，在采集过程中紧密复制真实场景，并采用数据增强技术来模拟各种外部条件。此外，我们展示了如何将RecyBat24的面向检测的注释用于创建RecyBat24的第二个版本以用于实例分割任务。最后，我们证明了最近的轻量级机器学习模型能够实现高精度，突出了它们在计算资源受限的分类和分割应用中的潜力。
