Photovoltaic modules inevitably produce various defects in daily operation, and hot spot defects are one of them. The existing research mainly focuses on the defects of photovoltaic modules in the production process, and there is few research on the defect detection algorithms generated by PV modules in daily operation, and there are problems such as poor generalization ability and insufficient accuracy. Based on the original faster RCNN, this study combines image preprocessing, migration learning, improved feature extraction network model, and improved anchor frame selection scheme to obtain hot spot defect detection model. Experiments show that the average detection accuracy of the self-made test set using this model is 97.34%, which is 4.51% higher than the original faster RCNN.