Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage

With the rapid development and innovation of the lithium battery industry in recent years, battery safety performance testing has become increasingly important. As an essential component of lithium batteries, Mylar films can significantly improve the safety of lithium batteries. However, few studies have focused on damage detection in Mylar films. To address this issue, this study developed an innovative intelligent detection method for lithium battery Mylar film damage. This method utilizes computed tomography (CT) nondestructive testing technology to accurately obtain internal information on lithium batteries. Subsequently, by combining image-preprocessing techniques and deep learning algorithms, an intelligent detection model was constructed to efficiently and accurately detect defective batteries. Experimental results demonstrate that the proposed method achieves a high detection rate and low false-detection rate for Mylar film defects, highlighting its significant potential for practical applications.