A wind turbine blade defect detection technique, EPW-YOLOv7, based on the YOLOv7 network model, is suggested for the features of wind turbine blade defects with large scale variation and complicated backdrop in order to achieve real-time accurate identification of wind turbine blade flaws. Firstly, the CSM attention module is designed and added to the backbone network to suppress the complex background interference and to improve the efficiency of extracting important features. Second, the lightweight PWK module is designed to replace the original （ELAN） module to reduce the amount of redundant parameters and computation, and to accelerate the detection speed of the network. Then, the bidirectional feature pyramid networks（BiFPN） feature fusion module is introduced to motivate the network to recognize multi-scale defect features more accurately. Finally, the wise-intersection over union（WIoU） loss function is used to optimize the network and improve the overall detection performance of the improved model. The wind turbine blade defect dataset is used for experiments, and the results demonstrate that the average accuracy of the EPW-YOLOv7 model can reach 88.4%, which is 7.1% higher than that of the YOLOv7-tiny model. Additionally, the frame rate reaches 66 frames/s, satisfying the requirement for real-time detection. In addition, compared with the current state-of-the-art target detection algorithms, the EPW-YOLOv7 model can detect defects of wind turbine blades with high accuracy and faster. In addition, compared with the current advanced target detection algorithms, the EPW-YOLOv7 model has more advantages in detecting the defects of wind turbine blades in terms of accuracy and speed, which demonstrates that the proposed algorithm is more suitable for the real-time detection and localization of wind turbine blade defect.