研究人员开发了一种用于在X射线图像中检测和分类电池的迁移学习方法。该方法利用预先训练的YOLOv5m模型，在一个用于电子设备检测的数据集上进行微调，然后识别出方形、软包和圆柱形锂离子电池。该技术在电池检测方面实现了94%的精度，比基础YOLOv5m模型提高了5%，推理时间为22毫秒。 AI
影响 提高了工业X射线成像中自动电池识别的准确性和速度。
排序理由 该集群包含一篇详细介绍新研究方法和结果的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.11779v1 Announce Type: new Abstract: 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 identifyi…
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, …