Abstract: In order to solve the problem of low efficiency and recognition accuracy in unmanned aerial vehicle inspection of photovoltaic modules,the paper propose a defect detection method for the photovoltaic modules based on super-resolution and dual-pooling fusion.Firstly,data augmentation technology（DAT）is used to expand the image data of the photovoltaic modules,and establish an image dataset that can be used for defect target detection in photovoltaic power plants. Then an image super-resolution network is constructed to reduce the noise in the image dataset and improve the texture features of local regions. Finally,the backbone network of the object detection framework is replaced with a feature extraction network fused in a dual pooling manner（VGG19-MP）,learning deeper texture structures without increasing network parameters. The results shows that the accuracy of the proposed method is 98.21%,and an average detection time is 0.066 seconds. Compared with the comparative detection algorithms,the accuracy improves by 0.9～9.1%,and the average detection time increases by 0.01～0.07 seconds,providing a more effective detection method for the precise identification of photovoltaic module defects.