Journal of Guangxi Normal University(Natural Science Edition) ›› 2019, Vol. 37 ›› Issue (2): 105-112.doi: 10.16088/j.issn.1001-6600.2019.02.012

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Defect Detection of Solar Panel Based on Machine Vision

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

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2018-04-04 Online:2019-04-25 Published:2019-04-28

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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

CLC Number: 

  • TP391
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