Abstract: To address the challenges of complex feature handling and diverse defect representations in wind turbine blade surface defect detection using drones,this paper introduces a novel approach based on multi-feature fusion and ensemble learning.The proposed method integrates local LBP features,HOG features,and high-level features from capsule networks into a comprehensive multi-feature extraction model,enhancing detail resolution.Additionally,three base classifiers with distinct bias and variance characteristics－support vector machine(SVM),k-nearest neighbors(KNN),and decision tree(DT)－can utilized to construct a heterogeneous ensemble learning model,leveraging the strengths of each base model to improve overall performance.Validation on a wind turbine blade surface defect dataset reveals that the multi-feature extraction model(MFEM) achieves an average precision(AP) of 98%,outperforming YOLOv7 and Faster R-CNN by 3.1% and 5.8%,respectively,and demonstrating substantial improvements over individual SVM,KNN,and DT models.Ablation studies further confirm the effectiveness of the proposed model.The results underscore the superior performance of the MFEM in wind turbine blade defect detection tasks.

Key words: Drones, Fan blade, Defect detection, Multi-feature fusion, Integrated learning, Capsule network