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arXiv · 2606.11779

Battery detection of XRay images using transfer learning

Nermeen Abou Baker·David Rohrschneider·Uwe Handmann
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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 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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Nermeen Abou Baker, David Rohrschneider, Uwe Handmann. 2026-06-10. Battery detection of XRay images using transfer learning. https://doi.org/10.14428/esann%2F2022.es2022-60

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