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Wind turbine blades are the core components of wind power generation systems, which are susceptible to various defects such as cracks, wear, and corrosion due to factors such as climate conditions and working loads. If these defects cannot be detected and resolved in a timely manner, it will lead to a decrease in fan performance, damage, and even accidents. To this end, a method for automatic detection of surface defects on fan blades based on drones is studied. Using a drone equipped with a camera, fly high into the air to capture images of the blades moving in the air. Implement grayscale, denoising, and illumination equalization processing on leaf images to improve image quality. Extract geometric and texture features from leaf images. Using differential evolution algorithm to improve the smoothing parameters of probability neural network, constructing a classification recognition model based on the optimized probability neural network, taking geometric and texture features as inputs, calculating the output probability of each category, and using the maximum response principle to determine the defect category with the highest probability value, in order to achieve automatic detection of surface defects on wind turbine blades. The results show that under the application of the studied technology, the Jaccard coefficient is relatively higher, indicating that the detection results of this method are more accurate; The relatively less time spent indicates that this method has higher detection efficiency and can complete detection tasks faster.