[1]檀甫贵、邹复民、刘丽桑、李建兴.基于机器视觉的软包锂电池表面缺陷检测[J].福建工程学院学报,2020,18(03):267-272.[doi:10.3969/j.issn.1672-4348.2020.03.012]
　TAN Fugui,ZOU Fumin,LIU Lisang,et al.Surface defect detection of soft-pack lithium battery based on machine vision[J].Journal of FuJian University of Technology,2020,18(03):267-272.[doi:10.3969/j.issn.1672-4348.2020.03.012]
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基于机器视觉的软包锂电池表面缺陷检测()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第18卷
期数:
2020年03期
页码:
267-272
栏目:
出版日期:
2020-06-25

文章信息/Info

Title:
Surface defect detection of soft-pack lithium battery based on machine vision
作者:
檀甫贵、邹复民、刘丽桑、李建兴
福建工程学院信息科学与工程学院
Author(s):
TAN Fugui;  ZOU Fumin;  LIU Lisang;  LI Jianxing
School of Information Science and Engineering, Fujian University of Technology
关键词:
软包锂电池; 缺陷检测; 机器视觉; 边缘检测
Keywords:
soft-packed lithium battery;  defect detection;  machine vision;  edge detection
分类号:
TP391
DOI:
10.3969/j.issn.1672-4348.2020.03.012
文献标志码:
A
摘要:
针对软包锂电池表面缺陷检测，基于机器视觉技术提出了一种改进的自动检测方法。 对图像进行预处理后，将Canny 算子检测法和Close_Edges 算子检测法相结合，分割出软包锂电池表面的缺陷?最后以最小外接矩形法计算出划痕的长度和宽度，以累加法计算出针孔的直径。 实验结果表明该方法能够有效分割出软包锂电池表面的划痕和针孔，缺陷尺寸计算的误差低于5%。
Abstract:
An improved automatic detection method based on machine vision technology was proposed for detecting surface defects of soft-pack lithium batteries. After the image was preprocessed, the Canny operator detection method and the Close_Edges operator detection method were combined to segment the defects on the surface of the soft-pack lithium battery; finally, the length and width of the scratches were calculated by the minimum external rectangle method, and then the diameter of the pinhole was calculated by the accumulation method. Experimental results show that this method can effectively detect the scratches and pinholes on the surface of the soft-pack lithium battery, and the error of the defect size calculation is mostly within 5%.

参考文献/References:

[1] 李克斌, 余厚云, 周申江. 基于形态学特征的机械零件表面划痕检测[J]. 光学学报, 2018, 38(8): 260-266[2] 冯凯萍, 吕笑文, 张丽文, 等. 一种新的光学元件表面划痕检测算法[J]. 电脑知识与技术, 2018, 14(1): 230-232.[3] 王思宇, 郭阳宽, 郭会梁, 等. 手机屏幕表面划痕检测系统研究[J]. 数字技术与应用, 2018(4): 57-58.[4] 廖声洋, 韩震宇, 董先飞. 基于机器视觉的高速宽幅铝箔针孔检测系统[J]. 计测技术, 2013, 33(5): 49-52.[5] 卢颖颖, 孙育. 基于机器视觉的电镀件表面缺陷检测系统[J]. 电镀与环保, 2019, 39(2): 59-61.[6] 罗菁, 董婷婷, 宋丹, 等. 表面缺陷检测综述[J]. 计算机科学与探索, 2014, 8(9): 1041-1048.[7] LUKAC R. Adaptive color image filtering based on center-weighted vector directionalfilters[J]. Multidimensional Systems and Signal Processing, 2004, 15(2): 169-196.[8] 黄玉龙. 基于视频图像的管道裂纹缺陷检测方法研究[D]. 西安: 西安理工大学, 2018. [9] 常云浩. 铁路轨道近景影像边缘提取与线形计算方法研究[D]. 成都: 西南交通大学, 2015. [10] 范勋. 实验小鼠体态特征图像识别算法研究[D]. 南京: 南京理工大学, 2012.

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更新日期/Last Update: 2020-06-25