| 李大华,吴超强,高强,于晓.改进YOLOv8n的风机桨叶表面缺陷轻量化检测网络[J].电子测量与仪器学报,2025,39(8):145-155 |
| 改进YOLOv8n的风机桨叶表面缺陷轻量化检测网络 |
| Improved YOLOv8n lightweight detection network forwind turbine blade surface defects |
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| DOI： |
| 中文关键词: 风机桨叶 目标检测 YOLOv8n 轻量化 注意力机制 |
| 英文关键词:wind turbine blade target detection YOLOv8n lightweight attention mechanism |
| 基金项目: |
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| Author | Institution |
| Li Dahua | 1.School of Electrical Engineer and Automation，Tianjin University of Technology, Tianjin 300384,China;2.Tianjin Key Laboratory
of New Energy Power Conversion，Transmission and Intelligent Control，Tianjin University of Technology,Tianjin 300384,China |
| Wu Chaoqiang | 1.School of Electrical Engineer and Automation，Tianjin University of Technology, Tianjin 300384,China;2.Tianjin Key Laboratory
of New Energy Power Conversion，Transmission and Intelligent Control，Tianjin University of Technology,Tianjin 300384,China |
| Gao Qiang | 1.School of Electrical Engineer and Automation，Tianjin University of Technology, Tianjin 300384,China;2.Tianjin Key Laboratory
of New Energy Power Conversion，Transmission and Intelligent Control，Tianjin University of Technology,Tianjin 300384,China |
| Yu Xiao | 1.School of Electrical Engineer and Automation，Tianjin University of Technology, Tianjin 300384,China;2.Tianjin Key Laboratory
of New Energy Power Conversion，Transmission and Intelligent Control，Tianjin University of Technology,Tianjin 300384,China |
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| 中文摘要: |
| 针对当前风机桨叶缺陷检测算法存在的检测精度不足、复杂背景下误检漏检发生率高和模型不便部署等问题,提出一种改进YOLOv8n的风机桨叶缺陷检测算法。首先,引入MobilenetV2中的关键模块加以改进提出全新的额外残差（Extra-IB）模块和C2f-Extra-IB模块,用于减少模型参数量实现轻量化并为后续特征融合通过高质量特征图;其次提出自适应高效多尺度空间金字塔（AEMFP）模块替换快速空间池化金字塔（SPPF）模块,该模块创新地采用融合高效多尺度注意力机制（EMA）和并行子结构的设计,提高算法的多尺度特征融合和特征自适应提取能力;最后在颈部网络引入高效局部注意力机制（ELA）,削弱复杂环境对于检测效果的影响同时提高对小目标的检测精度。使用风机桨叶表面缺陷数据集进行消融实验和对比实验,所提算法平均精度均值（mAP）达到81.7%,相较于YOLOv8n提升5.1%,模型参数量和浮点计算数分别为2.09×106和5.4 GFLOPS,减少22.3%和21.7%,模型体积减少19.8%,检测帧速达到45.57 fps,说明所提改进措施可以在保证算法检测精度提高的同时实现轻量化,满足使用无人机等计算资源有限的检测设备进行高效、精确的风机桨叶缺陷检测的需求。 |
| 英文摘要: |
| Aiming at the problems of the current fan blade defect detection algorithm, such as insufficient detection accuracy, high incidence of false detection under complex background and inconvenient deployment of model, an improved YOLOv8n fan blade defect detection algorithm was proposed. Firstly, a new Extra-IB module and C2f-Extra-IB module are introduced to improve the key modules in MobilenetV2, which are used to reduce the number of model parameters to achieve lightweight and pass high-quality feature maps for subsequent feature fusion. Secondly, the AEMFP module is proposed to replace the SPPF module, which innovatively integrates the EMA attention mechanism and parallel substructure design to improve the multi-scale feature fusion and feature adaptive extraction capability of the algorithm. Finally, ELA attention mechanism is introduced into the neck network to reduce the influence of complex environment on the detection effect and improve the detection accuracy of small targets. Ablation experiments and comparison experiments were conducted using fan blade surface defect data set. The proposed algorithm mAP reached 81.7%, an increase of 5.1% compared with YOLOv8n. The number of model parameters and floating-point calculations were 2.09×106 and 5.4 GFLOPS, respectively, decreasing by 22.3% and 21.7%. The model size is reduced by 19.8% and the detection frame speed reaches 45.57 frames.It shows that the improvement measures proposed in this paper can not only improve the detection accuracy of the algorithm, but also achieve lightweight, which can meet the demand of using the detection equipment with limited computing resources such as UAV for efficient and accurate fan blade defect detection. |
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