储能科学与技术 ›› 2025, Vol. 14 ›› Issue (6): 2512-2514.doi: 10.19799/j.cnki.2095-4239.2025.0482

• 储能测试与评价 • 上一篇    下一篇

基于电子图像处理技术的锂电池表面缺陷检测

平金珍(), 温沁润()   

  1. 石家庄信息工程职业学院软件工程系,河北 石家庄 052161
  • 收稿日期:2025-05-22 修回日期:2025-05-29 出版日期:2025-06-28 发布日期:2025-06-27
  • 通讯作者: 温沁润 E-mail:wqrlucky1974@126.com;wenqinrun001@163.com
  • 作者简介:平金珍(1980—),女,硕士,副教授,主要研究方向为计算机软件开发,E-mail:wqrlucky1974@126.com

Surface defect detection of lithium battery based on electronic image processing technology

Jinzhen PING(), Qinrun WEN()   

  1. Shijiazhuang Information Engineering Vocational College, Software Engineering Department, Shijiazhuang 052161, Hebei, China
  • Received:2025-05-22 Revised:2025-05-29 Online:2025-06-28 Published:2025-06-27
  • Contact: Qinrun WEN E-mail:wqrlucky1974@126.com;wenqinrun001@163.com

摘要:

随着锂离子电池工业制造水平的日益提升,对电池零部件的检测速度与精度要求也在不断提高。极片作为锂离子电池的关键部件,其表面缺陷种类繁多且面积微小,传统的目视检测已难以满足现阶段的极片生产需求。而基于图像处理的锂电池表面缺陷检测技术很好地解决了上述问题,本文针对该技术的研究进行了分析综述。首先介绍了锂电池表面缺陷检测技术的发展情况,然后从各个环节详细阐释了电子图像处理技术下的锂电池表面缺陷检测流程,包括图像采集、预处理、特征提取与识别等步骤。通过实际研究可以证实,通过优质的图像处理和分析算法,可以较为准确地检测锂电池缺陷问题,适合目前工业生产需求。

关键词: 锂电池, 缺陷, 检测, 采集

Abstract:

With the increasing level of lithium-ion battery industrial manufacturing, the requirements for the detection speed and accuracy of battery components are also increasing. As a key component of lithium-ion batteries, the pole piece has a wide variety of surface defects and a small area, and the traditional visual inspection has been difficult to meet the current production needs of the pole piece. The surface defect detection technology of lithium battery based on image processing can solve the above problems well, and the research of this technology is analyzed and reviewed in this paper. Firstly, the development of lithium battery surface defect detection technology is introduced, and then the lithium battery surface defect detection process under electronic image processing technology is explained in detail from each link, including image acquisition, preprocessing, feature extraction and recognition. Through practical research, it can be confirmed that through high-quality image processing and analysis algorithms, lithium battery defects can be detected more accurately, which is suitable for the current industrial production needs.

Key words: lithium battery, flaw, detect, gather

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