BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and Recycling
This work addresses the costly and manual challenge of battery sorting for recycling industries, though it is incremental in applying existing methods to a new domain.
The paper tackles the problem of automating battery sorting for recycling by addressing data scarcity, achieving an average accuracy of 92.1% and up to 96.2% in battery-type classification.
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.