基于独立分量分析和粒子群算法的太阳能电池表面缺陷红外热成像检测

根据红外成像特性及太阳能电池电致发光原理,研究一种基于限制式独立分量分析(ICA)模型和粒子群优化(PSO)方法的太阳能电池组件表面缺陷检测方法。利用太阳能电池红外图像的结构特点,首先设计一种ICA滤波器,并使用具有多方向搜索特性的PSO算法来求解ICA的分离矩阵,求解中加入限制式,使图像正常区域经滤波后有一致的反应值并有效凸显缺陷区域。然后使用ICA滤波器对图像进行旋积运算,最后使用阈值分割得到检测结果。实验结果表明,提出的ICA滤波检测方法对太阳能电池组件表面缺陷检测效果显著,检测精度高,能很好地区分背景和缺陷。

Detection System for Solar Module Surface Defects Based on Constrained ICA Model and PSO Method

According to the characteristics of infrared imaging and the electroluminescent principle of solar module, a detection system based on constrained independent component analysis (ICA) model and particle swarm optimization (PSO) method for solar module surface defects is proposed. Firstly, a constrained ICA filter is designed on the basis of the structure characteristics of infrared image for solar cells, and PSO method with multidirectional search characteristics is used to find the best demixing matrix for ICA. The constraint incorporated in the ICA model confines the source values of all training image patches of a defect-free image within a small interval of control limits and highlights the value of defect region. Then, ICA filter is used to convolve with the image. Finally, defect results are obtained through threshold segmentation. Experimental result shows that the proposed method can effectively detect the defects on solar module surface.