The need for detecting and sorting batteries is drastically increasing for many applications. This study proves the potential of transfer learning in predicting whether the image contains a battery or not, the location and identifying three types of batteries, namely: prismatic, pouch, and cylindrical Lithium-Ion Batteries (LIB). Particularly, it focuses on the transfer learning method in two applications: Training a large-scale dataset to detect electronic devices using a pre-trained YOLOv5m, then using these latter trained weights to detect and classify the batteries. The precision of battery detection achieves 94%, which outperforms the pretrained YOLOv5m weights with 5%, in 22 ms inference time.

翻译：电池检测与分类需求在众多应用中急剧增长。本研究证明了迁移学习在预测图像中是否包含电池、定位电池以及识别三种类型锂离子电池（棱柱形、软包形和圆柱形）方面的潜力。特别地，本文聚焦于迁移学习在两种场景中的应用：利用预训练的YOLOv5m训练大规模数据集以检测电子设备，随后采用训练后的权重对电池进行检测与分类。电池检测精度达到94%，相比预训练YOLOv5m权重提升5%，推理耗时22毫秒。