CN104952754B - 基于机器视觉的镀膜后硅片分选方法 - Google Patents
基于机器视觉的镀膜后硅片分选方法 Download PDFInfo
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- CN104952754B CN104952754B CN201510224553.XA CN201510224553A CN104952754B CN 104952754 B CN104952754 B CN 104952754B CN 201510224553 A CN201510224553 A CN 201510224553A CN 104952754 B CN104952754 B CN 104952754B
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- 230000005540 biological transmission Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N carbon Chemical compound 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- H—ELECTRICITY
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Abstract
本发明公开了基于机器视觉的镀膜后硅片分选方法，属于机器视觉分选技术领域，其采用彩色图像坐标变换技术、彩色图像分割技术、彩色图像RGB空间分析技术、图像处理技术、大数据逻辑统计分析与处理技术等对采集的图像进行处理和分析；提取图像的色彩信息；所得信息经过图像二值化、滤波处理筛选和换算；所得有用信息经过基于样本大数据逻辑统计分析和处理的算法执行后，将镀膜后硅片分为不合格片、均匀红片、合格片三类。本发明通过快速采集镀膜后硅片表面的彩色图像信息，可实时在线稳定高效地进行准确的硅片缺陷识别和分选，且实时显示检测结果；还可通过数据采集设备与机械手通讯，使其对应不同的类别快速采取不同的机械动作。
Description
基于机器视觉的镀膜后硅片分选方法
技术领域
[0001]本发明属于机器视觉分选技术领域，具体涉及基于机器视觉的镀膜后硅片分选方 法。
背景技术
[0002] 硅片是太阳能电池生产的主要原料，其镀膜质量的优劣直接决定后续工序中电池 片印刷的质量，从而影响太阳能电池的性能，因此须对镀膜后硅片进行分选，将存在缺陷的 不合格桂片予以剔除。但由于石墨舟各个槽的温度不一样、外界环境等因素的影响，锻膜后 硅片会出现发黄片、发红片、发白片、碎片、表面污渍等不合格片或均匀红片，这些硅片品种 的多样性和复杂性导致对其检测方法的不确定性。 >
[0003]目前很多太阳能电池片生产厂家主要还是以人工检测为主，这也带来了检测标准 不确定、效率低、碎片多、成本高等问题，故而很难达到工业生产的标准和要求。
发明内容
[0004] 发明目的：本发明的目的在于提供一种基于机器视觉的镀膜后硅片分选方法，其 具有稳定性高、与硅片非接触式、速率快等优点，能够实时在线、快速准确、高效稳定地检测 镀膜后硅片的缺陷和类别，自动对硅片进行分选，并自动将不合格产品取出，放入指定硅片 盒内。
[0005] 技术方案:为实现上述发明目的，本发明采用如下技术方案：
[0006] 基于机器视觉的镀膜后硅片分选方法，包括如下步骤：
[0007] 步骤201、接收信号，采集图像，其包括：
[0008] 步骤2011，镀膜后硅片到达传感器位置，传感器发送模拟信号给数据采集设备，经 由采集卡转换为数字信号传递给系统；
[0009]步骤2012,系统接收到采集信号后，触发相机，采集图像，并将采集的镀膜后硅片 彩色图像传送至图像处理单元；
[0010] 步骤202、对镀膜后硅片的彩色图像进行坐标变换和图像分割，其包括：
[0011]步骤2〇21，对镀膜后硅片的彩色图像进行坐标变换，采用自动查找边缘算法找出 镀膜后硅片的一边，获得其角度信息;其方法是先确定一个搜索区域，在搜索区域内，从上 往下设置若干搜索线，查找像素的跃迀点，之后将所有搜索线上的跃迁点拟合为一条直线， 得到所得直线的角度信息;其角度信息为：
[0012] anglei = 0 (1)
[0013] 然后利用公式⑵，将图像旋转，进行坐标变换，为图像分割做准备；
[0014] angle —360-0 (2)
[0015]步骤2〇22,采用实时自动查找图像边缘算法分别对镀膜后硅片的四条边进行边缘 查找;获得各自边缘线的坐标信息;其坐标信息为：
[0016] lineleft: (xu，yu)，（xi2，yi2)⑶
[0017] lineright: (X2i，y2i)，（X22，y22)⑷
[0018] linetop: (X3i，y3i)，（X32，y32)⑸
[0019] linebottom: (X4i，y4i)，（X42，y42)⑹
[0020] 其中lineleft，lineright，1 inetop，linebottom分别为所得到的四条边缘线段 (左、右、上、下）的两个顶点坐标；
[0021] 步骤2023,分别以式(3)、（4)、（5)、⑹所得的两个顶点坐标为基础，按照式⑺所 示求得四条边缘直线Y1 (左边缘）、Yr (右边缘）、Yt (上边缘）、Yb (下边缘）；
[0022] y=ax+b (7)
[0023]基于所得到的四条边缘直线方程，依次求取Y1与丫1：、Yr与Yb的交点m、n;以点m和点 n为分割图像的起始点和终止点，得到镀膜后硅片与背景分离出来的本体图像；
[0024] 步骤2〇3、以彩色图像RGB空间为载体将上述所获得的镀膜后硅片图像分成R、G、B 三个平面，分别获得三个平面的灰度值信息；
[0025]步骤204、对上述步骤203获得的B平面二值化，采用中值滤波器对所得二值化图像 进行滤波去噪处理，使图像在保证原有信息的条件下最大限度减少噪声影响和外界环境的 千扰；
[0026]步骤2〇5、对所获得滤波处理后的二值化图像进行信息采集，得到其像素和信息， 根据该数据判断硅片是否为碎片，是碎片则归为不合格一类；
[0027]步骤2〇6、分别对所获得的完整镀膜后硅片R、G、B平面进行灰度信息采集，得到像 素值为0的频率值与整个图像像素和的比值；
[0028]步骤2〇7、将上述步骤得到的比值与由样本大数据处理分析得到的逻辑关系进行 一系列的比对和判断，得到镀膜后硅片的分选结果。
[0029]步骤2〇7中，所述的镀膜后硅片的分选结果分为不合格、合格、均匀红片三类；当分 为不合格类时，将发送信号给机械臂，将其吸入指定不合格硅片盒；当分为均匀红片类时， 将发送信号给机械臂，将其吸入指定均匀红片硅片盒；当分为合格类时，将不发送任何信号 给机械臂，让其直接流入花篮。
[0030]发明原理:本发明的分选的对象是经过石墨舟镀膜后的硅片，其分选方案采用了 彩色图像分割技术、彩色图像RGB空间分析技术、图像处理技术、大数据逻辑统计分析与处 理技术等对实时采集的镀膜后硅片图像进行处理和分析，将镀膜后硅片分为不合格片、均 匀红片和合格片三类。其中，步骤207中，样本大数据处理与统计分析是基于大量实体样本 按照上述所述步骤采集到的R、G、B三个平面各自图像信息的分析、归纳和验证的。
[0〇31^有益效果:与现有技术相比，本发明的基于机器视觉的镀膜后硅片分选方法，通过 快速采集镀膜后硅片表面的彩色图像信息，可实时在线稳定高效地进行准确的硅片缺陷识 别和分选，且实0寸显示检测结果，自动将分选类别分为均匀红色、不合格品和合格品三类， 还可通过数据采集设备与机械手通讯，使其对应不同的类别快速采取不同的机械动作。
附图说明
[0032]图1是镀膜后硅片检测分选流程图；
[0033]图2是硅片到达传感器位置侧视图；
[0034]图3是镀膜后硅片经过步骤2021中查找边缘之后的结果图像；
[0035]图4是镀膜后硅片经过步骤2021中坐标变换前后的对比图像；
[0036]图5是步骤2023中镀膜后硅片四周边缘查找结果图像；
[0037]图6是步骤2023中镀膜后硅片与背景分离的结果图像。
具体实施方式
[0038]以下结合附图和具体实施方式对本发明做进一步的说明。
[0039]如图1所示，基于机器视觉的镀膜后硅片的分选方法，包括如下步骤：
[0040] 步骤201、接收信号，采集图像，其包括：
[0041]步骤2011，镀膜后硅片到达传感器位置，如图2所示，白色背景板1，光电传感器2， 镀膜后硅片3;传感器发送模拟信号给数据采集设备，经由采集卡转换为数字信号传递给系 统；
[0042]步骤2012,系统接收到采集信号后，触发相机，采集图像，并将采集的镀膜后硅片 彩色图像传送至图像处理单元；
[0043] 步骤2〇2、对镀膜后硅片的彩色图像进行坐标变换和图像分割，其包括：
[0044] 步骤2〇21，对镀膜后硅片的彩色图像进行坐标变换，采用自动查找边缘算法找出 镀膜后硅片的一边，获得其角度信息；其方法是先确定一个搜索区域，在搜索区域内，从上 往下设置若干搜索线，查找像素的跃迁点，之后将所有搜索线上的跃迁点拟合为一条直线 4,如图3所示，得到所得直线4的角度信息，直线4是由像素跃迁点拟合而成的直线;其角度 信息为：
[0045] anglei = 9 (1)
[0046] 然后利用公式(2)，将图像旋转，进行坐标变换，如图4所示，图4的左侧图像为坐标 变换前的情形，图4的右侧图像为坐标变换后的情形，为图像分割做准备；
[0047] angle = 360—0 ⑵
[0048] 步骤2022,采用实时自动查找图像边缘算法分别对镀膜后硅片的四条边进行边缘 查找;获得各自边缘线的坐标信息;其坐标信息为：
[0049] lineleft: (xii，yu)，（xi2，yi2)⑶
[0050] lineright: (X21，y2i)，（X22，y22)⑷
[0051] linetop: (X3i，y3i)，(X32，y32) (5)
[0052] linebottom: (X4i，y4i)，（X42，y42) (6)
[0053] 其中line left，liner ight，line top，linebottom分别为所得到的四条边缘线段 (左、右、上、下）的两个顶点坐标；
[0054] 步骤2023,分别以式(3)、（4)、⑸、（6)所得的两个顶点坐标为基础，按照式⑺所 示求得四条边缘直线H (左边缘）、Yr (右边缘）、Yt (上边缘）、Yb (下边缘），如图5所示；
[0055] y = ax+b (7)
[0056] 基于所得到的四条边缘直线方程，依次求取Y1与Yt、Yr与Yb的交点m、n;以点m和点 n为分割图像的起始点和终止点，得到镀膜后硅片与背景分离出来的本体图像，如图6所示， 图6的左侧图像为包含背景的镀膜后硅片图像，图6右侧为去除背景的镀膜后硅片本体图 像。
[0057] 步骤203、以彩色图像RGB空间为载体将上述所获得的镀膜后硅片图像分成R、G、B 三个平面，分别获得三个平面的灰度值信息；
[0058]步骤204、对上述步骤203获得的B平面二值化，采用中值滤波器对所得二值化图像 进行滤波去噪处理，使图像在保证原有信息的条件下最大限度减少噪声影响；
[0059]步骤2〇5、对所获得滤波处理后的二值化图像进行信息采集，得到其像素和信息， 根据该数据判断硅片是否为碎片，是碎片则归为不合格一类；
[0060]步骤206、分别对所获得的完整镀膜后硅片R、G、B平面进行灰度信息采集，得到像 素值为〇的频率值与整个图像像素和的比值;在R、G、B各个平面中像素值为0的地方表示该 区域为黑色，其在彩色图像中对应的区域颜色越深；
[0061] 步骤207、将上述步骤得到的比值与由样本大数据处理和统计分析得到的逻辑关 系进行比对和判断，得到镀膜后硅片的分选结果，镀膜后硅片分为不合格、合格、均匀红片 三类；当分为不合格类时，将发送信号给机械臂，将其吸入指定不合格硅片盒；当分为均匀 红片类时，将发送信号给机械臂，将其吸入指定均匀红片硅片盒；当分为合格类时，将不发 送任何信号给机械臂，让其直接流入花篮。
[0062] 经过以上七个步骤，避免了人工对镀膜后硅片缺陷检测和分类的不确定性，极大 地降低了人工接触式分选造成的碎片率，同时满足了在线生产、在线检测、在线分选的工作 效率，且快速、稳定、易操作。
Claims (2)
1.基于机器视觉的镀膜后硅片分选方法，其特征在于，包括如下步骤： 步骤201、接收信号，采集图像，其包括： 步骤2011，镀膜后硅片到达传感器位置，传感器发送模拟信号给数据采集设备，经由采 集卡转换为数字信号传递给系统； 步骤2012,系统接收到采集信号后，触发相机，采集图像，并将采集的镀膜后硅片彩色 图像传送至图像处理单元； 步骤202、对镀膜后硅片的彩色图像进行坐标变换和图像分割，其包括： 步骤2021，对镀膜后硅片的彩色图像进行坐标变换，采用自动查找边缘算法找出镀膜 后硅片的一边，获得其角度信息；其方法是先确定一个搜索区域，在搜索区域内，从上往下 设置若干搜索线，查找像素的跃迁点，之后将所有搜索线上的跃迁点拟合为一条直线，得到 所得直线的角度彳目息;其角度伯息为： anglei = 9 (1) 然后利用公式(2)，将图像旋转，进行坐标变换，为图像分割做准备； angle = 360-0 (2) 步骤2022,采用实时自动查找图像边缘算法分别对镀膜后硅片的四条边进行边缘查 找;获得各自边缘线的坐标信息;其坐标信息为： lineleft: (xu，yu)，（xi2，yi2) (3) lineright: (X2i，y2i)，（X22，y22) (4) linetop: (X3i，y3i)，（X32，y32) (5) linebottom: (X4i，y4i)，（X42，y42) (6) 其中lineleft，lineright，linetop，linebottom分别为所得到的左、右、上、下四条边 缘线段的两个顶点坐标； 步骤2〇23,分别以式(3)、（4)、（5)、⑹所得的两个顶点坐标为基础，按照式⑺所示求 得四条边缘直线:左边缘直线H、右边缘直线Yr、上边缘直线Yt、下边缘直线Yb; y = ax+b (7) 基于所得到的四条边缘直线方程，依次求取Y1与Yt、Yr与Yb的交点m、n;以点m和点n为 分割图像的起始点和终止点，得到镀膜后硅片与背景分离出来的本体图像； 步骤2〇3、以彩色图像RGB空间为载体将上述所获得的镀膜后硅片图像分成R、G、B三个 平面，分别获得三个平面的灰度值信息； 步骤204、对上述步骤203获得的B平面二值化，采用中值滤波器对所得二值化图像进行 滤波去噪处理，使图像在保证原有信息的条件下最大限度减少噪声影响和外界环境的干 扰； 步骤20f5、对所获得滤波处理后的二值化图像进行信息采集，得到其像素和信息，根据 该数据判断硅片是否为碎片，是碎片则归为不合格一类； 步骤206、分别对所获得的完整镀膜后硅片R、G、B平面进行灰度信息采集，得到像素值 为0的频率值与整个图像像素和的比值； 步骤207、将上述步骤得到的比值与由样本大数据处理分析得到的逻辑关系进行一系 列的比对和判断，得到镀膜后硅片的分选结果。
2.根据权利要求1所述的基于机器视觉的镀膜后硅片分选方法，其特征在于:步骤207 中，所述的镀膜后娃片的分选结果分为不合格、合格、均匀红片三类;当分为不合格类时，将 发送信号给机械臂，将其吸入指定不合格硅片盒;当分为均匀红片类时，将发送信号给机械 臂，将其吸入指定均勾红片娃片盒；当分为合格类时，将不发送任何信号给机械辟、卜甘古 接流入花篮。 < &化具直
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