Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.

电磁发光（EL）电池的缺陷检测是太阳能电池模块生产和准备过程中的核心步骤，以确保电池的转换效率和较长的使用寿命。然而，由于缺乏针对小特征缺陷的特征提取能力，传统的单发多盒检测器（SSD）算法在高精度的EL缺陷检测中表现不佳。因此，提出了一种在深度学习框架内进行特征融合改进的改进SSD算法，以提高EL多类缺陷的识别率。对于EL，建立了包含通过旋转，去噪和二值化具有四种不同类型缺陷的图像的数据集。该算法通过特征金字塔网络的思想可以大大提高小尺度缺陷的检测精度。对EL缺陷检测的实验研究表明了该算法的有效性。此外，一项比较研究表明，该方法在检测EL缺陷方面优于其他传统检测方法，例如SIFT，Faster R-CNN和YOLOv3。