Solar Cell Defects Detection Based on Photoluminescence Images and Upgraded YOLOv5 Model

Abstract

Solar cells (SCs) are prone to various defects, which affect energy conversion efficiency and even cause fatal damage to photovoltaic modules. In this paper, photoluminescence (PL) imaging is used to visualize SC defects, based on which a detection method based on the YOLOv5 model is explored. At the same time, five data enhancement methods such as Mosaic, Mixup, HSV transformation, Gaussian noise, and rotation transformation are introduced to improve the representativeness of the data set and enhance the detection ability of the model. Second, a C2f module is designed to enhance the network model’s ability to fuse features. In order to further improve the convolutional network’s ability to capture target features, a series SPPF module combined with soft pooling is proposed to reduce the number of repeated operations, improve network efficiency, and focus on extracting higher level features from the input. Experimental results show that the optimized model’s mAP reaches 91.5%, which is 20.3% higher than the original model. The mAP increase of some defect types reaches 50.4%, and the detection speed reaches 24.2 FPS. The model’s defect detection capability for SC has been significantly enhanced, meeting the speed requirements at the same time.