基于机器视觉的太阳能电池片表面缺陷检测的研究
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摘要
随着一次性能源面临枯竭及社会发展对能源需求的不断增长,太阳能作为一种普遍均匀、清洁环保的绿色资源,得到了很多国家的重视和开发利用。作为太阳能的发电载体,太阳能电池片也成为人们关注的热点。太阳能电池片的质量是影响太阳能电池组件发电效率的主要因素之一,所以加强对太阳能电池片质量的检测是生产中一个必不可少的环节。本文以机器视觉为理论对太阳能电池片的表面缺陷问题做了相关的研究工作。
针对本系统中太阳能电池片表面的强反射特性,考虑了光源照明技术对表面缺陷检测的影响,采用了白色LED环形光源。
由于太阳能电池片表面具有规律性、纹理性强的特点,所以本文采用了针对具体缺陷分类检测的方法。针对缺角,通过对待测图像进行图像分割、形态学和中值滤波处理得到缺角缺陷图像。对于裂纹和断栅,首先将待检测图片按照栅线方向进行逐列扫描,得到灰度曲线,分析缺陷特征,采用一定步长得到灰度差分图像,通过阈值分割将灰度突变大的像素点(即灰度差分值大的点)分割出来,根据已得到的缺角缺陷,消除缺角对裂纹和断栅缺陷图像的影响,最后对该缺陷图像进行形态学和滤波处理,进一步消除噪声。
对于二值缺陷图像本文进行缺陷标记和识别。其中,选取了相应的特征参数作为判别缺陷类型的依据。本文设计了一个基于Matlab的太阳能电池片表面缺陷检测系统操作界面,通过该界面可以得到待检测图片的缺陷检测结果图像、缺陷个数和缺陷特征参数数据。
通过实验证明本系统对太阳能电池片表面的缺角、裂纹和断栅都有较高的识别率,并基本达到实时性要求。
With the exhaustion of non-renewable resources and the increment for energy, solar energy, as a kind of general, equal and ecological energy, has aroused many countries’attention and got development. As its carrier, solar cells have become a focus. The quality of solar cells has become one of main factors which affect the efficiency of solar modules, so it is very important to enhance inspection for solar cells in the production process. Based on machine vision this article does some research on the defects detection for the surface of solar cells.
Aiming at strong reflection of solar cell surface and considering the influence of light source, this article has used a white, ring LED lamp.
Because of regular, strong textures characteristic of the surface of solar cell, this article uses different methods for different defects. For broken corners, this article adopts image segmentation, morphology and median filtering to get broken corner image. For cracks and broken grids, First of all, the paper scan image sequentially along the direction of grid lines, analyze the features of defects, get grayscale difference curves from which this system can extract the coordinate of grayscale catastrophe points, then, remove the effect of broken corner by using broken corners’coordinates. Finally, to eliminate interference factors this system process defect image with morphology and median filtering.
For defect image, this paper has did some work about defect labeling and defect identification by choosing some corresponding feature parameters as the basis of judging defect types. Based on Matlab this article designed a system interface for the surface inspection of solar cells, from which readers can acquire defects image, defects number and statistical data about defects.
Experiments proved that this system can detect broken corners, cracks and broken grids at a higher recognition rate and achieve real-time demand basically.
针对本系统中太阳能电池片表面的强反射特性,考虑了光源照明技术对表面缺陷检测的影响,采用了白色LED环形光源。
由于太阳能电池片表面具有规律性、纹理性强的特点,所以本文采用了针对具体缺陷分类检测的方法。针对缺角,通过对待测图像进行图像分割、形态学和中值滤波处理得到缺角缺陷图像。对于裂纹和断栅,首先将待检测图片按照栅线方向进行逐列扫描,得到灰度曲线,分析缺陷特征,采用一定步长得到灰度差分图像,通过阈值分割将灰度突变大的像素点(即灰度差分值大的点)分割出来,根据已得到的缺角缺陷,消除缺角对裂纹和断栅缺陷图像的影响,最后对该缺陷图像进行形态学和滤波处理,进一步消除噪声。
对于二值缺陷图像本文进行缺陷标记和识别。其中,选取了相应的特征参数作为判别缺陷类型的依据。本文设计了一个基于Matlab的太阳能电池片表面缺陷检测系统操作界面,通过该界面可以得到待检测图片的缺陷检测结果图像、缺陷个数和缺陷特征参数数据。
通过实验证明本系统对太阳能电池片表面的缺角、裂纹和断栅都有较高的识别率,并基本达到实时性要求。
With the exhaustion of non-renewable resources and the increment for energy, solar energy, as a kind of general, equal and ecological energy, has aroused many countries’attention and got development. As its carrier, solar cells have become a focus. The quality of solar cells has become one of main factors which affect the efficiency of solar modules, so it is very important to enhance inspection for solar cells in the production process. Based on machine vision this article does some research on the defects detection for the surface of solar cells.
Aiming at strong reflection of solar cell surface and considering the influence of light source, this article has used a white, ring LED lamp.
Because of regular, strong textures characteristic of the surface of solar cell, this article uses different methods for different defects. For broken corners, this article adopts image segmentation, morphology and median filtering to get broken corner image. For cracks and broken grids, First of all, the paper scan image sequentially along the direction of grid lines, analyze the features of defects, get grayscale difference curves from which this system can extract the coordinate of grayscale catastrophe points, then, remove the effect of broken corner by using broken corners’coordinates. Finally, to eliminate interference factors this system process defect image with morphology and median filtering.
For defect image, this paper has did some work about defect labeling and defect identification by choosing some corresponding feature parameters as the basis of judging defect types. Based on Matlab this article designed a system interface for the surface inspection of solar cells, from which readers can acquire defects image, defects number and statistical data about defects.
Experiments proved that this system can detect broken corners, cracks and broken grids at a higher recognition rate and achieve real-time demand basically.
引文
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4贾云得.机器视觉.北京:科学出版社, 2000: 23~30
5刘泉,胡文娟.基于机器视觉的PCB缺陷检测系统设计与研究.电子器件. 2007, 30(2): 550~553
6甘英俊,胡天翔,沈海涛,等.基于机器视觉的汽车牌照识别.机械制造与自动化. 2009, 38(2): 97~101
7胡传双,云虹,赵俊石,等.基于激光扫描成像技术的锯材裂缝和孔洞缺陷的机器视觉识别系统.华南农业大学学报. 2008, 28(4): 108~112
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10吴海龙,周荷琴,张鹿.从CT图像中自动分割出肺部区域的算法研究.航天医学和医学工程. 2008, 21(5): 425~429
11张耀,王耀南,周博文.异型瓶药液中可见异物的智能视觉检测机器人.仪器仪表学报. 2010, 5: 1058~1063
12李旭,张为公.基于视觉的驾驶机器人导航技术.光电工程. 2008, 35(2): 56-60
13王荣本,张荣辉,金立生.区域智能交通车辆系统发展及其关键技术综述.汽车工程. 2007, 29(3): 179~182
14赵海涛,瘀东军,金忠,等.基于形状和纹理的人脸识别.计算机研究与发展. 2003, 40(4): 538~543
15罗希平,田捷.自动指纹识别中的图像增强与细节匹配算法.软件学报. 2002, 13(5): 946~956
16 R. Ahmed, M. P. F. Sutcliffe. Identification of Surface Features on Cold-Rolled Stainless Steel Strip. Wear. 2000, 244: 60~70
17 J. Badger, S. Enright. Automated Surface Inspection System. Iron and Steel Engineer. 1996, 3: 70~73
18 M. Vascotto. High Speed Surface Detect Identification on Steel Strip. MPT. 2005, 4: 70~73
19 B. Souheil, L. Reinhold. Infrared Detection of Defects in Powder-Metallic Compacts. Journal of Nondestructive Eval. 2007, 26(1): 1~9
20 P. Cavaliere, A. De. Santis, F. Panella, et al. Thermoelasticity and CCD Analysis of Crack Propagation in AA6082 Friction Stir Welded Joints. International Jouranl of Fatigue. 2009, 31(2): 385~392
21张世强,李万河,徐品烈.硅太阳能电池的丝网印刷技术.电子工业专用设备. 2007, 36(5): 55~59
22 M. A. Ordaz, G. B. Lush. Machine vision for solar cell character relation. The 8th SPIE Conference on Machine Vision Applications in Industrial Inspection, San Jose, 2000, 3966(2000): 238~248
23 J. P. Rakotoniaina, O. Breitenstein, MHA. Rifai, et al. Detection of Crack in Silicon Wafers and Solar Cells by Ultrasonic Lock-in Thermograph. Proceedings of the 19th European Photovoltaic Solar Energy Conference and Exhibition. Munchen, 2004, 640~643
24 M. Pilla, F. Galmiche, X. Malague. Thermographic Inspection of Cracked Solar Cells. Processing of SPIE. 2002, 4710: 699~703
25 A. Belyaev, O. Polupan, S. Ostapenko,et al. Resonance Ultrasonic Vibration Diagnostics of Elastic Stress in Full-size Silicon Wafers. Semiconductor Science and Technology. 2006, 21: 254~260
26 M. Du, C. C. Chang, S. M. Chao. Micro-crack Inspection in Heterogeneously Textured Solar Wafers Using Anisotropic Diffusion. Image and Vision Computing, 2010, 28: 491~501.
27 Z. Fu, Y. Z. Zhao, Y. Liu. Solar Cell Crack Inspection by Image Processing.International Conference on the Business of Electronic Product Reliability and Liability. 2004: 77~80
28 A. A. Istratov, H. Hieslmair, O. F. Vyvenko, et al. Defect Recognition and Impurity Detection techniques in Crystalline Silicon for Solar Cells. Solar Energy Materials & Solar Cells. 2002, 72: 441~451.
29 Q. Li, W. S. Wang, C. Ma, et al. Detection of Physical Defects in Solar Cells by Hyper spectral Imaging Technology. Optics & Laser Technology. 2010, 42: 1010~1013
30丁叶飞,李红波,张滢清,等.硅太阳电池组件工艺缺陷的红外检测技术.太阳能. 2010, 2: 36~37
31张舞杰,李迪,叶峰.硅太阳能电池视觉检测方法研究.计算机应用. 2010, 30(1): 249~252
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