Aiming at the problem of network overfitting and model performance under standard caused by the lack of defective data amount of solar cells, In this paper, a true and false data fusion algorithm based on deep convolution confrontation generation network and random image Mosaic is proposed, which improves the training data volume by 800 times. At the same time, the network model is optimized with light weight to reduce model training parameters. The experimental results show that the test accuracy of the trained model after the data fusion and expansion of the data set is nearly 30% and 17% higher than that of the original training set and the traditional data enhancement algorithm. After the lightweight treatment, the model parameters were reduced to about half of the previous ones, and the test time for each image was shortened from 57 ms to 22 ms. The research shows that the fusion algorithm can effectively alleviate the problem of network overfitting caused by insufficient training data. The lightweight optimization model not only ensures the accuracy, but also compresses the size of the model to speed up the test.