Abstract Aiming at the problem of color difference detection on polycrystalline silicon cells under complex color and texture characteristics, a new method based on support vector machine classification strategy is proposed to detect the color difference of polycrystalline silicon cells. Firstly, color model conversion and channel separation are performed on the pre-processed cell images. The Otsu method is used to perform threshold segmentation processing on the single-channel image, and the region contrast of each threshold image is calculated, and then an appropriate threshold image is selected according to the regional contrast condition. The image features are extracted by the information provided by the threshold image. Finally, the support vector machine classifier is used to determine whether the cell has a color difference defect. The experimental results show that the proposed color difference detection algorithm can achieve high-efficiency detection of color difference defect, and the accuracy, false detection rate and detection time reach 96.88%, 5% and 109 ms.
GUO Bao-su,WU Wen-wen,FU Qiang, et al. Color Difference Detection of Polycrystalline Silicon Cells Based on Support Vector Machine Classification Strategy[J]. Acta Metrologica Sinica, 2019, 40(6): 1013-1019.
［1］Tsai D, Lin M. Machine-vision-based identification for wafer tracking in solar cell manufacturing［J］. Robotics and Computer-Integrated Manufacturing, 2013, 29(5): 312-321.
［2］周奇. 基于HALCON的太阳能电池片缺陷检测系统设计［D］. 镇江: 江苏大学, 2017.
［3］孔祥伟. 基于反馈式多光谱成像的纱线颜色测量研究［J］. 计量学报, 2019, 40(1): 91-96.
Kong X W. Research on yarn color measurement based on feedback multi-spectral imaging［J］. Acta Metrologica Sinica, 2019, 40(1):91-96.
［4］Kaur H, Kamaldeep, Garima. Color Defect Detection of Pharmaceutical Capsules using Histogram based Thresholding［J］. Indian Journal of Science & Technology, 2016, 9(47):1-3.
［5］Moradi G, Shamsi M, Sedaaghi M H, et al. Using Statistical Histogram Based EM Algorithm for Apple Defect Detection［J］. American Journal of Signal Processing, 2012, 2(2): 10-14.
［6］长岭邦彦, 冨冈聪. 颜色不均匀检查装置及颜色不均匀检查方法［P］. 中国: CN102054175A, 2011.
［7］倪豪, 郑慧峰, 王月兵, 等. 基于自动种子区域生长的超声B图像缺陷分割方法［J］. 计量学报, 2018, 39(6):878-883.
Ni H, Zheng H F, Wang Y B, et al. Ultrasonic B image defect segmentation method based on automatic seeded region growing［J］. Acta Metrologica Sinica, 2018, 39(6):878-883.
［8］Zhang Z, Liu Y, Wu X, et al. Integrated color defect detection method for polysilicon wafers using machine vision［J］. Advances in Manufacturing, 2014, 2(4): 318-326.
［9］Wang H, Miao R, Wu X S, et al. Polycrystalline Silicon Wafer Surface Color Defect Inspection Based on Machine Vision［J］. Applied Mechanics and Materials, 2014, 548-549: 48-52.
［10］Lin H, Chien C. Automated Detection of Color Non-Uniformity Defects in TFT-LCD［C］ // The 2006 IEEE International Joint Conference on Neural Network, Vancouver, BC, Canada, 2006:1405-1412.
［11］高春甫,贺新升,唐可洪,等. 随机纹理图像的色差检测［J］. 光学精密工程, 2009,17(08): 1999-2003.
Gao C F, He X SH, Tang K H, etc. Color-difference Detection of Image with Random Texture［J］. Optics and Percision Engineering, 2009, 17(08): 1999-2003.
［12］Baly R, Hajj H. Wafer Classification Using Support Vector Machines［J］. IEEE Transactions on Semiconductor Manufacturing, 2012, 25(3): 373-383.
［13］王晓初, 王士同, 包芳, 等. 最小化类内距离和分类算法［J］. 电子与信息学报, 2016, 38(3): 532-540.
Wang X C, Wang S T, Bao F, et al. Intraclass-Distance-Sum-Minimization Based Classification Algorithm［J］. Journal of Electronics & Information Technology, 2016, 38(3): 532-540.
［14］程淑红, 高许, 周斌. 基于多特征提取和SVM参数优化的车型识别［J］. 计量学报, 2018, 39(3): 348-352.
Cheng S H, Gao X, Zhou B. Vehicle Recognition Based on Multi-feature Extraction and SVM Parameter Optimization［J］. Acta Metrologica Sinica, 2018, 39(3): 348-352.
［15］时培明, 梁凯, 赵娜, 等. 基于分形维数和GA-SVM的风电机组齿轮箱轴承故障诊断［J］. 计量学报, 2018, 39(1): 61-65.
Shi P M, Liang K, Zhao N, et al. Fault Diagnosis of Wind Turbine Gearbox Bearing Based on Fractal Dimension and GA-SVM［J］. Acta Metrologica Sinica, 2018, 39(1): 61-65.
［16］王书涛, 朱彩云, 刘洺辛, 等. 基于CS-SVM的山梨酸钾的荧光光谱检测法研究［J］. 计量学报, 2018, 39(5): 747-752.
Wang Sh T, Zhu C y, Liu M X, et al. Study on Fluorescence Spectrometric Detection of Potassium Sorbate Based on CS-SVM［J］. Acta Metrologica Sinica, 2018, 39(5): 747-752.
［17］Bergstra J, Bengio Y. Algorithms for hyper-parameter optimization［C］ //The 24th International Conference on Neural Information Processing Systems, Granada, Spain, 2011: 2546-2554.
［18］彭真明, 蒋彪, 肖峻, 等. 基于并行点火PCNN模型的图像分割新方法［J］. 自动化学报, 2008, 34(9): 1169-1173.
Peng Z M, Jang B, Xiao J, et al. A Novel Method of Image Segmentation Based on Parallelized Firing PCNN［J］. Acta Automatica Sinica, 2008, 34(9): 1169-1173.
［19］Yuan X, Wu L, Peng Q. An improved Otsu method using the weighted object variance for defect detection［J］. Applied Surface Science, 2015, 349: 472-484.
［20］Malarvel M, Sethumadhavan G, Bhagi P C R, et al. An improved version of Otsu’s method for segmentation of weld defects on X-radiography images［J］. Optik-International Journal for Light and Electron Optics, 2017, 142: 109-118.
［21］李春龙. 基于机器视觉的硅片表面缺陷检测和颜色检测［D］. 无锡: 江南大学, 2013.
［22］王晗. 多晶硅表面缺陷识别及软件检测系统设计与开发［D］. 上海: 上海交通大学, 2014.
［23］Platt J, Cristianini N, Shawe-Taylor J. Large Margin DAGs for Multiclass Classification［J］. Advances in Neural InformationProcessing Systems, 2000, 12(3): 553-574.
2019, Vol. 40 
