Battery detection of XRay images using transfer learning
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.