Aiming at the problem of low accuracy and poor classification effect of small size defect detection of offshore wind turbine blades, an improved early defect detection model of offshore wind turbine blade surface based on EfficientNet is proposed. Firstly, the asymmetric convolution is introduced into the EfficientNet feature extraction network to replace the ordinary 3 × 3 convolution, which enhances the convolution kernel skeleton information and improves the ability of the network to extract defect information. Secondly, a hybrid spatial channel attention module is proposed to focus on space and channel information, and the BiFPN feature fusion module is used to fuse the semantic information of different depths to improve the multi-scale feature fusion ability of the algorithm. Finally, Focal-EIOU and Focal Loss functions are introduced to calculate the position loss and classification loss, so as to improve the positioning accuracy and solve the problem of imbalance between positive and negative image samples in the model training process. The experimental results show that the average accuracy of the proposed algorithm model is 97.6%, and the detection performance of early defects on the surface of wind turbine blades is significantly improved.