用迁移学习实现电池类型与位置精准识别，速度达每秒45帧。
Battery detection of XRay images using transfer learning
- 基于预训练YOLOv5m模型迁移学习，提升检测精度。
- 电池检测准确率94%，比原始模型高5个百分点。
- 适用于电子废弃物分拣与安全筛查场景。
随着多种应用对电池检测与分类需求激增，本研究验证了迁移学习在判断图像是否含电池、定位电池位置及识别三类锂离子电池（方形、软包、圆柱形）方面的潜力。重点采用两种迁移学习策略：先用大规模数据集训练预训练的YOLOv5m以检测电子设备，再将训练好的权重用于电池检测与分类。实验结果表明，该方法在22毫秒推理时间内实现94%的检测精度，较原始YOLOv5m模型提升5个百分点。
原文摘要 · Abstract (English)
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.
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