CT理论与应用研究2025，Vol.34Issue(4)：551-559,9.DOI:10.15953/j.ctta.2025.061
基于CT扫描的锂电池Mylar膜破损智能检测方法
Intelligent Computed Tomography-based Detection Method for Lithium Battery Mylar Film Damage
摘要
Abstract
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.关键词
锂电池Mylar膜/缺陷检测/Retinex图像增强/图像分类Key words
Mylar films of lithium battery/defect detection/Retinex enhancement/image classification分类
信息技术与安全科学引用本文复制引用
李梦磊,夏迪梦,林国杨,赵树森..基于CT扫描的锂电池Mylar膜破损智能检测方法[J].CT理论与应用研究,2025,34(4):551-559,9.基金项目
国家自然科学基金数学天元基金交叉重点专项(AI驱动的锂电池跨尺度模拟与关键材料设计(12426301)) （AI驱动的锂电池跨尺度模拟与关键材料设计(12426301）
深圳市优秀人才培养项目(新能源电池检测专用CT快速成像方法研究(RCBS20231211090724044)) （新能源电池检测专用CT快速成像方法研究(RCBS20231211090724044）
深圳市龙华区创新专项资金(20250113G43468522)). （20250113G43468522）
