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

In recent years, deep learning techniques have been extensively utilized 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 dataset for the detection and classification of three battery types: Pouch, Prismatic, and Cylindrical. Our dataset is designed with an application-oriented objective, simulating real-world scenarios during the acquisition process and employing data augmentation techniques to replicate various external conditions. Additionally, we illustrate how this detection-focused annotation can be used to create a second version of RecyBat24 for instance-segmentation tasks. Finally, we demonstrate how recent lightweight deep learning models achieve high accuracy, highlighting their potential for classification and segmentation applications where computational resources are constrained.