To address the large parameter count, high computational overhead, and poor minor-defect detection in existing wind turbine blade surface anomaly detection models, this paper designs a lightweight detection model named CDFNet. The model takes YOLOv10n as the baseline and introduces a feature enhancement downsampling (FED) module into the backbone to enhance fine-grained feature extraction for complex anomaly regions. A cross-dimensional collaborative attention (CCA) mechanism integrates spatial location with channel semantic information to improve minor-defect localization and recognition. The method also replaces the original neck with a fast bidirectional feature pyramid network (FBiFPN) to boost multi-scale feature fusion efficiency and reduce model complexity. Experiments on a self-built dataset of wind turbine blade surface anomalies show that CDFNet achieves a mAP@0.5 of 83.2%, which is 6.4 percentage points higher than the baseline. CDFNet contains 2.02×106 parameters, a 25.5% reduction compared to the baseline. For minor defects, CDFNet improves detection accuracy over the baseline by 21.3 percentage points for cracks, 11.6 for missing diversion strips, and 11.1 for surface damages. These results demonstrate that CDFNet strikes a favorable balance between detection accuracy and model lightweightness, making it suitable for wind turbine blade surface anomaly detection.