光伏电池电致发光偏振图像融合与缺陷检测

Photovoltaic cell electroluminescence polarization image fusion and defect detection

针对电致发光图像边缘模糊、纹理不清晰造成光伏电池缺陷难以定量化评估问题,提出一种基于电致发光偏振图像融合的晶硅光伏电池缺陷检测方法。首先,在分析晶硅光伏电池结构的基础上给出电致发光偏振成像的基本原理。然后,利用拉普拉斯金字塔对获取的红外光强图像与偏振度图像进行分解、引导滤波对高频细节成分进行增强,通过区域能量最大、区域能量加权平均规则对高低频部分进行融合。最后搭建短波红外偏振检测平台开展晶硅光伏电池缺陷检测实验。结果表明,偏振成像可以凸显光伏电池缺陷图像的轮廓边缘和纹理细节,融合图像中光伏电池缺陷特征更加突出,信息熵、标准差等客观评价指标显著提高,验证了方法的有效性。

The edge of electroluminescence image is fuzzy and the texture is not clear, which makes it difficult to quantitatively evaluate the defects of crystalline silicon photovoltaic cell. In order to solve this problem, a defect detection method based on electroluminescence polarization image fusion is proposed. First, based on the analysis of the crystalline silicon photovoltaic cell structure, the electroluminescence polarization imaging mechanism was introduced. Then, the Laplacian pyramid was used to decompose the obtained infrared intensity images and polarization images, and guide filter was used to enhance the high-frequency components. The high and low frequency parts were fused by the rule of regional energy maximum and regional energy weighted average. Finally, a short wave infrared polarization detection platform was established for photovoltaic cell inspection. The results show that polarization imaging can highlight the contour edges and texture detail of the photovoltaic cell defect image. The photovoltaic cell defect features in the fusion image are more prominent. The objective evaluation index such as information entropy and standard deviation are significantly improved, which verifies the effectiveness of the proposed method.