Lightweight CNN’s Superiority in Industrial Defect Detection: A Case Study of Wind Turbine Blades
Liang Du, Soon-Hyung Lee, Kyung-Min Lee, Yong-Sung Choi · Machines · 2026
This paper investigates the effectiveness of lightweight Convolutional Neural Networks (CNNs) compared with Vision Transformers (ViTs) for industrial defect detection, with a focus on wind turbine blades. While ViTs have recently attracted significant attention in computer vision research, their advantages over traditional CNNs remain unclear in highly specialized industrial applications. To address this gap, a rigorous comparative study was conducted using a labeled dataset of wind turbine blade surface defects, including corrosion, craze, hide_craze, surface_attach, surface_corrosion, surface_injure, surface_oil, thunderstrike. Experimental results demonstrate that lightweight CNNs outperform ViTs in both accuracy and efficiency. Specifically, CNN-based models achieved a maximum accuracy of 98.2%, while the best-performing ViT reached only 50.6%. Beyond accuracy, CNNs also showed superior data efficiency and robustness when trained on relatively small datasets, underscoring their suitability for industrial defect detection tasks where large-scale annotated data are often unavailable. These findings highlight the continuing relevance of lightweight CNNs in industrial settings and provide practical guidance for selecting models in safety-critical applications such as wind turbine blade inspection. This paper contributes by clarifying the limitations of ViTs under industrial conditions and reinforcing the value of lightweight CNNs as a reliable and computationally efficient solution for defect detection.