Research On Defect Detection And Color Sorting System Of Photovoltaic Solar Crystal Silicon Cell Based On Machine Vision

In order to solve the problem of automatic defect detection and color classification of silicon cells at the end of screen printing pipeline,through the analysis of solar cell detection methods,a solution based on machine vision for defect detection and color sorting of cells is proposed,and developing an inspection system of photovoltaic solar crystalline silicon cell.Firstly,analyzing the common defect types,color grades and their causes of formation of solar cells by introducing the preparation process of solar cells,afterwards putting forward the corresponding detection standards and requirements.At the same time the overall scheme design of photovoltaic solar crystal silicon cell detection system is carried out,and the system hardware and system software are analyzed and designed respectively.Then,the selection of core components,such as industrial camera,lens and gray card,and offline online software system design with detection function modular are completed according to the requirements of the detection system.secondly,the defect detection algorithm of solar crystalline silicon cells is discussed.First,introducing the preprocessing processes including color correction,region extraction,silicon wafer positioning,process point shielding and so on.After that,proposing a subpixel-based cell size measurement method.Aiming at the damage defects,the detection methods of morphology and reference template are used to carry out experiments,and the appropriate detection algorithm is selected through comparative analysis.Aiming at the defects of grid line printing,the detection steps are subdivided into grid line extraction,fingers detection and busbars detection according to the distribution characteristics of grid line.Aiming at the dirt defect,an improved local threshold segmentation method was extracted.Then,research on the color sorting algorithm of solar crystalline silicon cells.First the color histogram feature extraction is carried out using HSI channel through introducing the common color space and its transformation method.Then a color sorting algorithm based on neural network is advanced with the traditional color sorting algorithm analyzed,and the operation efficiency and accuracy of the two methods are compared and analyzed experimentally,which proves the superiority of the algorithm.Finally,aiming at the defect detection and color sorting system of solar crystal silicon cells in this topic,the comprehensive performance of the system was analyzed from the perspective of accuracy,efficiency and stability combined with online detection and manual eye inspection.Experimental data show that the comprehensive performance of the system can meet the actual production requirements.