BatSort: Enhanced Battery Classification with Transfer Learning for
  Battery Sorting and Recycling
Yunyi Zhao Wei Zhang Erhai Hu Qingyu Yan Cheng Xiang King Jet Tseng Dusit Niyato
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.