arXiv:2404.05802v1 Announce Type: cross
Abstract: 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.
Battery-Type Classification: A Machine Learning Approach
Battery recycling is a critical process that aims to minimize environmental harm and resource waste. However, the sorting of batteries based on their types has proven to be a challenging and costly task. In this paper, a machine learning-based approach called BatSort is introduced to address this issue.
BatSort utilizes transfer learning, which leverages the existing knowledge optimized with large-scale datasets, to classify battery types. The system customizes ResNet, a deep learning model, for the battery-type classification task. This approach is particularly useful as it tackles the problem of data scarcity, which is common in the battery sorting domain.
The authors collected an in-house battery-type dataset of small-scale to guide the knowledge transfer and conducted an experimental study to evaluate the performance of BatSort. The results show that the system achieves outstanding accuracy, with an average of 92.1%, and up to 96.2% accuracy for battery-type classification. Importantly, the performance of BatSort is stable, ensuring reliable and consistent results.
The multi-disciplinary nature of this research is worth highlighting. It combines concepts from machine learning, image classification, and battery recycling. By incorporating transfer learning, the authors bridge the gap between the wider field of multimedia information systems and the specific domain of battery sorting. This cross-disciplinary approach enhances the efficiency and effectiveness of the battery recycling process.
Furthermore, the application of BatSort extends beyond battery recycling. The concept of automated classification using machine learning can be adopted in other industries with insufficient data. The success of BatSort demonstrates the potential for similar approaches in optimizing resource utilization and minimizing cost in various sectors. Moreover, it opens doors for future research in related fields such as artificial reality, augmented reality, and virtual realities, where machine learning techniques can be further integrated.
In conclusion, the introduction of BatSort, a machine learning-based approach for battery-type classification, paves the way for fast and automated battery sorting with minimized cost. This innovation contributes to the wider field of multimedia information systems while addressing a critical challenge in battery recycling. The outstanding performance of BatSort emphasizes the potential of machine learning techniques in solving data scarcity issues in various industries. As technology continues to advance, it is expected that similar approaches will play a significant role in optimizing resource utilization and streamlining processes in multiple domains.