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In order to realize intelligent detection of surface defects of wind turbine blades, this study applied UAV technology, image vision technology and deep learning algorithm to establish a defect detection system for wind turbine blades, which improved the detection accuracy of cracking defects on blades. The system uses an operator to calculate the horizontal and vertical gradients of the image, and performs threshold segmentation and denoising. The deep learning model was constructed to extract the feature information of image defects. Spp-net network was added for convolution operation, and the input data scale of the model was increased. After obtaining the feature image, PRN network was used to screen the feature image. The experimental results show that the system can remove a large amount of useless background information and retain the feature information of the crack defect position completely. After testing the images in the verification set, the number of crack defects identified by the system can reach 50 at most.