Battery detection of XRay images using transfer learning

2026-06-10

AI Summary

This study addresses the challenge of automatic detection, localization, and type identification of lithium-ion batteries in X-ray images by proposing a two-stage transfer learning approach. First, a YOLOv5m model is fine-tuned on a large-scale dataset of electronic devices; subsequently, the adapted model is transferred to the X-ray domain to jointly detect and classify three battery types—prismatic, pouch, and cylindrical. The proposed method achieves a detection accuracy of 94%, representing a 5% improvement over the original pretrained model, while maintaining a real-time inference speed of 22 milliseconds per frame. This approach effectively balances high accuracy with computational efficiency, offering a practical and scalable solution for intelligent battery sorting systems.

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.

Problem

• battery detection
• X-ray images
• Lithium-Ion Batteries
• transfer learning
• battery classification

Innovation

• transfer learning
• YOLOv5m
• battery detection
• X-ray imaging
• Lithium-Ion Batteries