Solar energy is an attractive source of electricity. Solar photovoltaic cells are the basis of solar power generation systems. However, various types of defects in solar photovoltaic cells seriously affect the photoelectric conversion efficiency and service life of photovoltaic cells. To effectively detect these defects, a defect detection method based on a block case deletion model is proposed. First, the solar photovoltaic cell image using Fourier transform is preprocessed, it removes the bus bar and adjusts the brightness and contrast, and divides the image into blocks. Then, in the processed image, all abnormal blocks are found and all of them are removed by using the case deletion model. The background of the image is reconstructed from the remaining image patches by a non-linear regression model. Finally, the defect area is highlighted by the difference between the image waiting for checking and the resulting background image. The experimental results show that the proposed method can effectively detect many kinds of defects in Solar cells, such as micro-cracks, breaks and fragment, etc. the method is used to experiment with 313 solar photovoltaic cell images. For 158 non-defective images, the test results are normal. The remaining 155 images containing defects such as cracks and broken gates have only 5 images mis-detected, and the detection rate of defective images is 96. 77%.