Defect detection in battery electrode production using supervised and unsupervised learning with laser speckle photometry data

The push for zero-emission transport is largely driven by the adoption of battery-powered vehicles. A critical aspect of a successful battery storage system is the production of high-quality electrodes, which necessitates rigor-ous inspection processes and defect detection systems. In this paper, we present data obtained using laser speckle photometry (LSP) technology and perform defect detection using two approaches: the YOLOv4 model and the newly developed U2S-CNNv2 model. The U2S-CNNv2 model combines unsupervised and supervised learning to identify defects beyond the training dataset. Our goal is to develop an efficient detection of defects for battery electrode production to meet stringent quality control standards. Our findings show that YOLOv4 is highly effective for deployment in inspection processes, capturing very small defects and operating at 50 frames per second (fps). YOLOv4 achieved an impressive 93.82% accuracy in correctly detecting and 91.10% in correctly labeling defects. Conversely, the U2S-CNNv2 model excels in precisely localizing defect areas and identifying unknown defects or patterns not included in the training dataset. However, it operates at a slower pace of around 3 fps and has a detection accuracy of 83.83% and correct labeling rate of 54.84%.