基于阈值均匀局部二值模式和BP神经网络的太阳电池缺陷检测算法  

DEFECT DETECTION OF SOLAR CELL BASED ON THRESHOLD UNIFORM LBP AND BP NEURAL NETWORK

作　　者：墨恺 徐林[1] 

机构地区：[1]上海交通大学物理系太阳能研究所,上海200240

出　　处：《太阳能学报》2014年第12期2448-2454,共7页Acta Energiae Solaris Sinica

摘　　要：提出一种阈值均匀局部二值模式(TULBP)算法。该算法使用均一模式以减少特征值的数量,在保证特征描述准确性的同时,可大大加快计算速度。在该算法基础上,提出基于BP神经网络的缺陷检测算法,使用矩形窗口提取特征值,将复杂的缺陷模式判断转化为神经网络模式识别问题。实验结果表明,该算法在使用单层BP神经网络时,即可达到较高的准确性,抗噪声能力强,适用范围广。In order to detect solar cell defects precisely and break through the limitation of low resolution, an algorithm called Threshold Uniform Local Binary Pattern （TULBP） was proposed. The algorithm used uniform patterns to reduce quantity of characteristic values so that it could ensure accuracy of feature description and quicken computing speed at same time. A BP neural network method with a rectangle testing window to detect defects was also proposed based on TULBP, which transferred the defects detection problems to a pattern recognition problem. The experiment results show that this method can reach high accuracy rate with single layer BP neural work.

关 键 词：局部二值模式 BP神经网络 太阳电池 缺陷检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]