基于机器视觉的太阳能网版缺陷检测

doi:10.16088/j.issn.1001-6600.2019.02.012

广西师范大学学报（自然科学版） ›› 2019, Vol. 37 ›› Issue (2): 105-112.doi: 10.16088/j.issn.1001-6600.2019.02.012

基于机器视觉的太阳能网版缺陷检测

朱勇建^*, 彭柯, 漆广文, 夏海英, 宋树祥

广西师范大学电子工程学院,广西桂林 541004

收稿日期:2018-04-04 出版日期:2019-04-25 发布日期:2019-04-28
通讯作者: 朱勇建(1979—),男, 江西瑞昌人,广西师范大学教授,博士。E-mail:52949534@qq.com
基金资助:
国家自然科学基金(51775230,61275110);广西研究生教育创新计划项目(XYCSZ2019070);桂林市科学研究与技术开发计划重点项目(2016010604,20170104-2);广西师范大学重点项目(2015ZD004);广西自然科学基金(2017GXNSFAA198313)

Defect Detection of Solar Panel Based on Machine Vision

ZHU Yongjian^*, PENG Ke, QI Guangwen, XIA Haiying, SONG Shuxiang

College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004, China

Received:2018-04-04 Online:2019-04-25 Published:2019-04-28

摘要/Abstract

摘要： 为了解决传统太阳能网版缺陷检测法效率低、检测速度慢和准确率低的问题,本文提出一种硬件与软件相结合的基于机器视觉的太阳能网版检测方法。根据测量精度的要求,本文还设计了一台适合太阳能网版图像采集的移动平台,软件部分主要包括缺陷检测、栅线宽度测量。利用质心检测和直线拟合测量栅线的宽度,在此基础上,通过支持向量机(SVM)图像分类法检测太阳能网版缺陷,利用已经分类好的样本进行训练生成一个分类器。经过实验验证,缺陷检测的准确率超过95%,栅线宽度测量误差为1 μm左右,证明该方法不仅具有检测成本低、可靠性高、检测效率高等特点,而且具有实用推广价值。

关键词: 太阳能网版, 支持向量机, 缺陷检测, 栅线宽度测量

Abstract: In order to solve the problem of low efficiency, low detection speed and low accuracy of traditional solar panel defect detection method, this paper presents a method for the detection of solar panel based on machine vision,which adopts the method of combining hardware and software. According to the requirement of measurement precision, a mobile platform is designed which is suitable for shooting solar panel images,where the software part includes defect detection and measurement of grid line width. The width of the grid line is measured by centroid detecting and fitting a straight line. Based on this, the defect of the solar panel is detected by the support vector machine (SVM) image classification system, and a classifier is generated by using classified samples. Experimental verification shows that the defect detection accuracy is higher than 95%, and the grid line width measurement error is 1 μm. It is proved that the method not only is of low cost, high reliability and high detection efficiency, but also has wide practical value.

Key words: solar panel, support vector machine, defect detection, grid width measurement

中图分类号:

TP391

朱勇建, 彭柯, 漆广文, 夏海英, 宋树祥. 基于机器视觉的太阳能网版缺陷检测[J]. 广西师范大学学报（自然科学版）, 2019, 37(2): 105-112.

ZHU Yongjian, PENG Ke, QI Guangwen, XIA Haiying, SONG Shuxiang. Defect Detection of Solar Panel Based on Machine Vision[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(2): 105-112.

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基于机器视觉的太阳能网版缺陷检测

Defect Detection of Solar Panel Based on Machine Vision

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