Defect Detection of Photovoltaic Solar Cells Based on Improved YOLOv5
Aiming at the problems of false detection and missed detection caused by unbalanced sample data,large difference in defect size and complex background in photovoltaic cell defect detection,a photovoltaic cell defect detection algorithm based on improved YOLOv5 is proposed. Firstly,in the data preparation stage,the BEGAN generative adversarial network is used to enhance the data,expand the defect image dataset,and deal with the problems of imbalance and defect size difference between classes. Secondly,the BiFPN bidirec-tional feature pyramid network is used in the neck network,which extracts different levels of feature informa-tion to fuse more defect features,thereby reducing the interference of the complex background of PV modules and improving the detection performance. Finally,a small target detection head is added to the model detec-tion output layer to reduce the loss of small and weak defect information,avoid feature confusion,and im-prove the detection accuracy. The experimental results show that the improved detection model is applied to the detection of EL defect dataset after data enhancement and enrichment,and the comprehensive performance index F1 reaches 84.43％,which improves the accuracy and recall rate by 3.02％ and 7.13％,respectively,and the detection accuracy mAP@0.5 increases by 4.31％ compared with the traditional YOLOv5 algorithm.
万方数据
维普
