To address the challenges of low accuracy， missed detection， and false detection in defect identification of wind turbine blade， an enhanced algorithm based on YOLOv8 is proposed. Initially， a DE-C2f module is introduced， replacing the bottleneck structure with a dual convolution kernel design based on efficient multi-scale attention， thereby improving the network's multi-scale feature extraction capability. Subsequently， a global receptive field feature fusion module （GRE-SPPF） is implemented to enhance the capture of global feature information and expand the receptive field. Further improvements include the addition of a small-object detection layer and a multi-scale feature fusion structure in the Neck， optimizing detection performance for small and complex objects. An attention and convolution fusion module （ACFM） is also integrated before the detection head to prioritize critical information while mitigating background interference. Experimental results on a wind turbine blade defect dataset indicate that the proposed algorithm achieves mAP@0.5 and mAP@0.5∶0.95 values of 91.1% and 61.8%， respectively， marking improvements of 6.2% and 6.4% over the baseline algorithm. The recall rate reaches 84.9%， a 7.7% enhancement， with no substantial increase in computational parameters， demonstrating the algorithm's efficacy for practical wind turbine blade defect detection.