基于改进YOLOv5s模型的风电叶片内腔缺陷检测

Wind turbine blade internal defect detection based on improved YOLOv5s model

针对风电叶片轻微裂纹难于检测的问题,提出一种基于改进YOLOv5s模型的检测方法:通过在主干网络部分使用空洞空间金字塔池化(ASPP)代替空间金字塔池化(SPP)以适应不同大小和比例的目标,将注意力机制模块(squeeze and excitation,SE)插入主干网络中以增加网络对微小缺陷的敏感度,使用结构化交并比损失(SIoU-Loss)代替完全交并比损失(CIoU-Loss),以进一步提高新网络的准确性和训练速度。针对以上检测方法采用自建数据集进行对比实验,实验结果表明,改进YOLOv5s模型的平均精度均值(mAP)为94.29%,与YOLOv5s模型相比提升了7.03个百分点,检测精度与其他的主流模型对比依然具有优势,检测速度为42.78 f/s。该方法在风电叶片内腔缺陷检测方面具有较好的使用性能和效果。

This paper proposes a detection method based on an improved YOLOv5s model for the problem of slight cracks in wind turbine blades that are difficult to detect.The method mainly includes three improvements.First,in the backbone network part,ASPP(atrous spatial pyramid pooling)is used instead of SPP(spatial pyramid pooling)to adapt to targets of different sizes and proportions.Second,SE(squeeze and excitation)attention modules are inserted into the backbone network to increase the network's sensitivity to small defects;and SloU-Loss is used to replace the original CloU-Loss to further improve the accuracy and training speed of the new network.Finally,a comparison experiment is conducted using a self-built dataset.Experimental results show that the mAP value of the improved YOLOv5s model is 94.29%,which is 7.03 percentage points higher than that of the YOLOv5s model,and its detection accuracy has advantages over other mainstream models.The detection speed is 42.78f/s.This method has good performance and effect in detecting defects inside wind turbine blades.