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

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...

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Bibliographic Details
Main Authors: Reed Pratt, Clark Allen, Mohammad A. S. Masoum, Abdennour Seibi
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Machines
Subjects:
deep learning
defect detection
fuzzy voting
image segmentation
Mask Region-based Convolutional Neural Network (R-CNN)
Residual Network 50-layer (ResNet-50)
Online Access:https://www.mdpi.com/2075-1702/13/4/283
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