English(EN) Battery detection of XRay images using transfer learning

迁移学习将X射线电池检测精度提升至94%

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-10 08:09

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 →

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arXiv cs.CV TIER_1 English(EN) · Nermeen Abou Baker, David Rohrschneider, Uwe Handmann · 2026-06-11 04:00

使用迁移学习对XRay图像进行电池检测

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…
arXiv cs.CV TIER_1 English(EN) · Uwe Handmann · 2026-06-10 08:09

使用迁移学习对XRay图像进行电池检测

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, …