Improved YOLOv8-GD deep learning model for defect detection in electroluminescence images of solar photovoltaic modules

Abstract

Photovoltaic defect detection is an essential aspect of research on building-distributed photovoltaic systems. Existing photovoltaic defect detection models based on deep learning, such as YOLOv5 and YOLOv8, have significantly improved the accuracy of photovoltaic defect detection. However, these models are too large, and their feature extraction ability is insufficient, leading to low detection efficiency and inability to cope with the continuous evolution of defects. Therefore, this study proposes an accurate and lightweight YOLOv8 (You Only Look Once v8) GD algorithm. The algorithm is an improved version of YOLOv8, wherein DW-Conv (DepthWise-Conv) is applied to the YOLOv8 backbone network. Moreover, convolution is replaced with the GSConv (Group-shuffle Conv) and the BiFPN (bidirectional feature pyramid network) structure is added to the architecture. Several electroluminescent photovoltaic defect datasets are used to verify the effectiveness of the proposed method. The final experimental results show that the map@0.5 and map@0.5∼0.95 of YOLOv8-GD are 92.8% and 63.1%, respectively, which are 4.2% and 5.7% higher than those of the original algorithm, respectively, and the model volume is reduced by 16.7%. Thus, the proposed algorithm shows considerable potential in the field of photovoltaic defect detection.