电子测量与仪器学报

韩鸿,王金明,张飞,李忠虎.基于跨维度特征交互的风机叶片异常状态检测轻量化模型[J].电子测量与仪器学报,2026,40(6):24-34

基于跨维度特征交互的风机叶片异常状态检测轻量化模型

Lightweight model for abnormal state detection of wind turbine bladesbased on cross-dimensional feature interaction

DOI：

英文关键词:wind turbine blades deep learning abnormal-state detection feature fusion feature interaction lightweight

基金项目:国家自然科学基金（62161042）、内蒙古自治区重点研发和成果转化计划项目（2025YFHH0061，2025YFHH0025）、内蒙古自然科学基金（2024LHMS06002）、高校基本科研业务费项目（2024QNJ005）资助

作者	单位
韩鸿	内蒙古科技大学自动化与电气工程学院包头014000
王金明	内蒙古科技大学自动化与电气工程学院包头014000
张飞	内蒙古科技大学自动化与电气工程学院包头014000
李忠虎	内蒙古科技大学自动化与电气工程学院包头014000

Author	Institution
Han Hong	School of Automation and Electrical Engineering，Inner Mongolia University of Science and Technology, Baotou 014000,China
Wang Jinming	School of Automation and Electrical Engineering，Inner Mongolia University of Science and Technology, Baotou 014000,China
Zhang Fei	School of Automation and Electrical Engineering，Inner Mongolia University of Science and Technology, Baotou 014000,China
Li Zhonghu	School of Automation and Electrical Engineering，Inner Mongolia University of Science and Technology, Baotou 014000,China

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中文摘要:

针对现有风机叶片表面异常状态检测模型参数量大、计算开销高以及对微小缺陷检测精度不足的问题，设计了一种轻量化检测模型CDFNet。该模型以YOLOv10n为基础，通过在骨干网络中引入特征增强下采样模块FED，以增强模型对复杂异常区域的细粒度特征提取能力；设计跨维度协同注意力机制CCA，通过融合空间位置信息与通道语义信息，提升模型对微小缺陷的定位与识别能力；采用快速双向特征金字塔网络FBiFPN替换原颈部网络，以提高模型的多尺度特征融合效率并降低模型复杂度。基于自建风机叶片表面异常状态数据集开展实验，并与多种主流目标检测模型进行对比验证。结果表明，CDFNet的mAP@0.5达到83.2%，较基线模型提升了6.4个百分点，模型参数量为2.02×106，较基线模型下降25.5%，对裂纹、导流条缺失和破损等微小缺陷的检测精度较基线模型分别提升21.3、11.6和11.1个百分点。实验结果表明，CDFNet在检测精度与模型轻量化之间实现了较好平衡，适用于风机叶片表面异常状态检测。

英文摘要:

To address the large parameter count, high computational overhead, and poor minor-defect detection in existing wind turbine blade surface anomaly detection models, this paper designs a lightweight detection model named CDFNet. The model takes YOLOv10n as the baseline and introduces a feature enhancement downsampling (FED) module into the backbone to enhance fine-grained feature extraction for complex anomaly regions. A cross-dimensional collaborative attention (CCA) mechanism integrates spatial location with channel semantic information to improve minor-defect localization and recognition. The method also replaces the original neck with a fast bidirectional feature pyramid network (FBiFPN) to boost multi-scale feature fusion efficiency and reduce model complexity. Experiments on a self-built dataset of wind turbine blade surface anomalies show that CDFNet achieves a mAP@0.5 of 83.2%, which is 6.4 percentage points higher than the baseline. CDFNet contains 2.02×106 parameters, a 25.5% reduction compared to the baseline. For minor defects, CDFNet improves detection accuracy over the baseline by 21.3 percentage points for cracks, 11.6 for missing diversion strips, and 11.1 for surface damages. These results demonstrate that CDFNet strikes a favorable balance between detection accuracy and model lightweightness, making it suitable for wind turbine blade surface anomaly detection.

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