MSWindD-YOLO: A Lightweight Edge-Deployable Network for Real-Time Wind Turbine Blade Damage Detection in Sustainable Energy Operations

Pan Li, Jitao Zhou (), Jian Zeng, Qian Zhao and Qiqi Yang
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Pan Li: School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China
Jitao Zhou: School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China
Jian Zeng: School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Qian Zhao: School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China
Qiqi Yang: School of Emergency Equipment, North China Institute of Science and Technology, Langfang 065201, China

Sustainability, 2025, vol. 17, issue 19, 1-31

Abstract: Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms.

Keywords: wind turbine blade; damage detection; YOLO; lightweight model; real-time detection; edge computing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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