Abstract:  In the process of manufacturing solar cells， due to the imperfect manufacturing processes and operational failure of humans， the defects， such as broken cell， crack， finger failure and silicon material missing might be found in the solar cells. A solar cell defect detection model based on YOLOv5s， namely YOLOv5s_CG， is proposed to improve the precision of the solar cell defects detection. The algorithm introduces convolutional attention mechanism （CBAM） blocks in different positions of the backbone network and feature fusion layer. The attention mechanism of the backbone network focuses on the global information， and the attention mechanism of the feature fusion layer focuses on the local information. At the same time， it enhances the features in both spatial and channel dimensions and uses the GIOU loss function to evaluate the detection effect of the algorithm. The proposed method is tested on the open source solar cell dataset which is re-labeled by the authors. The experimental results show that the overall mean average precision （mAP） of the YOLOv5s-CG algorithm reaches 75.1%. Compared with the algorithm of YOLOv5s， various types of defect detection accuracy have been improved， among which the accuracy of crack and silicon material missing has increased by 0.036 and 0.033 respectively， and the average accuracy （mAP） of all classes has increased by 0.026. Compared with the mainstream target detection algorithm of SSD， the overall mean average precision （mAP） has improved by 0.123. The algorithm can accurately detect the defects of solar cells， which could provide a better defects detection algorithm for real solar cell production.

Key words: solar cell defect detection, object detection, deep learning, YOLOv5, attention mechanism