Defect Detection and Classification on Wind Turbine Blades Using Deep Learning with Fuzzy Voting

Wind turbine inspections are traditionally performed by certified rope teams, a manual process that poses safety risks to personnel and leads to operational downtime, resulting in revenue loss. To address some of these challenges, this study explores the use of deep learning and drones for automated inspections. Three Mask R-CNN models, leveraging different convolutional neural network (CNN) backbones—VGG19, Xception, and ResNet-50—were constructed and trained on a novel dataset of 3000 RGB images (size 300 × 300 pixels) annotated with defects, including cracks, holes, and edge erosion. To improve defect detection performance, a multi-variable fuzzy (MVF) voting system is proposed. This method demonstrated superior accuracy compared to the individual models. The best-performing standalone model, Mask R-CNN with Xception, achieved an mAP of 77.48%, while the MVF system achieved an mAP of 80.10%. These findings highlight the effectiveness of combining fuzzy voting systems with Mask R-CNN models for defect detection on wind turbine blades, offering a safer and more efficient alternative to traditional inspection methods.