To address the issues of weakened defect features and localization difficulties in wind turbine blade drone inspections, this paper proposes a lightweight, highprecision detection algorithm based on the improved YOLO11 framework. Firstly, a mixed local channel attention (MLCA) module is introduced into the backbone to collaboratively model local texture details and global context, enhancing sensitivity to minor defects under complex lighting. Secondly, ADown progressive downsampling replaces standard convolutions to effectively preserve defect edge integrity while reducing feature map resolution. Finally, a multi-scale attention feature pyramid network (MAFPN) is constructed to improve semantic representation through cross-level feature aggregation. Experimental results on a real-world inspection dataset demonstrate that the proposed method achieves mAP@0.5 and mAP@0.5:0.95 scores of 85.7% and 61.1% (improvements of 4.0% and 5.6%, respectively), while reducing parameters by 22.5% and GFLOPs by 9.5%. The conclusion indicates that the algorithm significantly reduces missed and false detections in complex scenarios. Compared with representative models such as I-YOLOv8n and O-YOLO11, it achieves a superior accuracy-efficiency trade-off, making it highly suitable for deployment on edge devices like UAVs.