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Transfer learning boosts X-ray battery detection to 94% precision

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a transfer learning approach for detecting and classifying batteries in X-ray images. The method utilizes a pre-trained YOLOv5m model, fine-tuned on a dataset for electronic device detection, to then identify prismatic, pouch, and cylindrical Lithium-Ion Batteries. This technique achieved a 94% precision in battery detection, outperforming the base YOLOv5m model by 5% with an inference time of 22 milliseconds. AI

IMPACT Improves accuracy and speed for automated battery identification in industrial X-ray imaging.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and results.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Transfer learning boosts X-ray battery detection to 94% precision

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The cluster contains an academic paper detailing a new research methodology and results.
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paper, product
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94 days old
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Nermeen Abou Baker, David Rohrschneider, Uwe Handmann ·

    Battery detection of XRay images using transfer learning

    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…

  2. arXiv cs.CV TIER_1 English(EN) · Uwe Handmann ·

    Battery detection of XRay images using transfer learning

    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, …

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