中文摘要：

针对依赖人工进行太阳能电池片表面质量检测时效率和精度低的问题，文章提出了基于机器视觉以及人工神经网络的太阳能电池片表面质量检测方法。将表面缺陷分为外形缺陷、颜色缺陷、裂纹以及丝印线路缺陷4类，基于模板匹配检测外形缺陷，基于HIS空间下的颜色直方图检测颜色缺陷；针对细微性缺陷容易受噪声影响的特点，利用2类人工神经网络进行断栅检测，并对这2类神经网络进行比较。大量实验结果验证了上述方法能够准确、快速地检测出太阳能电池片表面缺陷。

英文摘要：

For the low efficiency and precision problem of solar cell surface test relying on manual la- bor, a method of the solar cell surface detection based on maehine vision and artificial neural network is raised. Surface defects are firstly divided into four categories, including appearance defects, color defects, cracks and defects of screen printing line. Then the appearance defects are detected based on template matching and the color detection is realized according to the image of color histogram on HIS space. Finally, for the characteristics of small defects which are easily affected by noise, two types of artificial neural networks are used to detect the broken gate and the two networks are compared. The experimental results show that the presented method can accurately and quickly detect the solar cell surface defects.