基于改进YOLOv8的风机桨叶缺陷检测算法
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华北电力大学自动化系 保定 071003

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TN911.73;TP391.4

基金项目:

国家自然科学基金面上项目(62373151)、国家自然科学基金联合项目(U21A20486)、中央高校基本科研业务费项目(2023JC006)、河北省自然科学基金(F2020502009,F2021502008)项目资助


Fan blade defect detection algorithm based on improved YOLOv8
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Department of Automation, North China Electric Power University,Baoding 071003, China

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    摘要:

    叶片作为风力发电机组的重要部件,容易受到自然环境的影响,导致出现侵蚀、裂纹、胶衣脱落等损伤,从而影响风力发电效率和机组的安全运行。针对复杂环境下风机桨叶缺陷检测精度较低的问题,提出了一种改进YOLOv8的风机桨叶缺陷检测算法。通过对骨干特征提取网络中的单一模块SPPF融入LSKA注意力机制,以增强网络对于重要特征的关注度,提高模型的性能;其次,Neck部分采用加权双向特征金字塔Bi-FPN结构,并使用FasterBlock改进C2f模块,提出了Bi-YOLOv8-faster轻量级网络结构,增强模型多尺度特征融合能力,提高小目标检测精度;最后,采用辅助边框计算损失的Inner-IoU方法对损失函数进行优化,提高模型缺陷检测的准确率和泛化能力。通过对风机桨叶图像进行缺陷检测实验,结果表明,所提方法对缺陷检测的精确率提升了7.3%、mAP50提升了3.3%、参数量降低了27%。

    Abstract:

    As an important component of wind turbines, blades are easily affected by the natural environment, leading to damage such as erosion, cracks, and detachment of rubber coats, thereby affecting the efficiency of wind power generation and the safe operation of the unit. A modified YOLOv8 fan blade defect detection algorithm is proposed to address the issue of low accuracy in detecting blade defects in complex environments. The single module SPPF in the backbone feature extraction network is integrated into the LSKA attention mechanism to enhance the network′s attention to important features and improve the performance of the model; Secondly, the Neck section adopts a weighted bidirectional feature pyramid Bi-FPN structure and use FasterBlock to improve the C2f module. The Bi-YOLOv8-faster lightweight network structure is proposed to enhance the multi-scale feature fusion ability of the model and improve the accuracy of small target detection; Finally, the Inner-IoU method, which assists in calculating the loss of bounding boxes, is used to optimize the loss function and improve the accuracy and generalization ability of the model′s defect detection. Through the experiment of defect detection on the image of fan blades, the results show that the proposed method improves the accuracy rate of defect detection by 7.3%, mAP50 by 3.3%, and reduces the number of parameters by 27%.

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李冰,张易牧,魏乐涛,王月,翟永杰.基于改进YOLOv8的风机桨叶缺陷检测算法[J].电子测量技术,2024,47(13):89-99

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  • 在线发布日期: 2024-11-07
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