Wind turbine blades (WTBs) are critical components of wind turbines. Exposed directly to harsh environmental conditions, they are susceptible to various defects. Timely detection of surface defects on WTBs is crucial for effective repair. Utilizing unmanned aerial vehicles (UAVs) for wind farm inspection offers greater efficiency and enhanced safety compared to manual inspections. However, the variability in angle and distance among images collected by UAVs results in significant variations in defect size and shape, including tilted targets and small targets. To address these issues, we propose a method for detecting surface defects on WTBs based on improved YOLOv7. To better characterize tilted defects, the angle prediction branch based on the phase-shifting coder (PSC) is integrated into the YOLOv7 to enable rotation of the target detection box. Additionally, efficient multi-scale attention (EMA) is adapted to prioritize defective parts and improve model precision. Finally, a normalized Wasserstein distance (NWD) loss function is introduced to mitigate the sensitivity to positional deviations and improve the detection precision for small defects. The experimental results show that the mean average precision (mAP) of the proposed algorithm reaches 89.1%, surpassing the original YOLOv7 algorithm and other rotating target detection algorithms.