Affected by the poor working environment and other reasons, the fan blades often have defects such as cracks and pits. Aiming at the low accuracy of the current common target detection algorithms for the detection of small-size defects of the fan blades, a fan blade defect detection method based on the EfficientDet algorithm is proposed. . First collect image data and establish a wind turbine blade defect image data set in Pascal VOC format, and then improve the backbone feature extraction network in the EfficientDet algorithm to reduce the number of downsampling and adjust the effective feature layer to enhance the backbone feature extraction network for small-size defects Detection capability; At the same time, the multi-scale feature fusion capability of the fusion path enhancement algorithm is added to the feature fusion network. The algorithm uses FReLU as the activation function to achieve pixel-level spatial information modeling, and uses Mosaic data enhancement and Focal Loss loss function to increase small-size defect samples for Contribution of the detector. The test results on the established defect image data set of fan blades show that the improved algorithm model has an average category accuracy of 96.15%, which is 3.77% higher than the original EfficientDet, and the detection performance of small targets has been significantly improved.