Work

RecyBat24: a dataset for detecting lithium-ion batteries in electronic waste disposal

Year: 2025

Type: data-paper

Abstract: 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.

Source: Scientific Data

DOI: 10.1038/s41597-025-05211-5

Authors: Ximena Carolina Acaro Chacón, Fabrizio Lo Scudo, Gregorio Cappuccino, Carmine Dodaro

Institution: University of Calabria

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,490