Battery recycling is a critical process for minimizing environmental harm and
resource waste for used batteries. However, it is challenging, largely because
sorting batteries is costly and hardly automated to group batteries based on
battery types. In this paper, we introduce a machine learning-based approach
for battery-type classification and address the daunting problem of data
scarcity for the application. We propose BatSort which applies transfer
learning to utilize the existing knowledge optimized with large-scale datasets
and customizes ResNet to be specialized for classifying battery types. We
collected our in-house battery-type dataset of small-scale to guide the
knowledge transfer as a case study and evaluate the system performance. We
conducted an experimental study and the results show that BatSort can achieve
outstanding accuracy of 92.1% on average and up to 96.2% and the performance is
stable for battery-type classification. Our solution helps realize fast and
automated battery sorting with minimized cost and can be transferred to related
industry applications with insufficient data.

我们提出了一种基于机器学习的电池类型分类方法，通过转移学习利用大规模数据集进行知识优化，并定制 ResNet 以针对电池类型进行分类。通过实验研究，结果表明，我们的解决方案在平均准确率达到 92.1％，最高可达 96.2％，适用于电池类型分类，能够实现快速自动化的电池分拣，降低成本，并可用于相关行业应用中的数据不足情况。