大型风电机组叶轮裂纹焊接图像检测技术研究

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中图分类号：TQ110.5 文献标志码：A 文章编号：1001-5922（2025）07-0033-04

Abstract：In order to improve theaccuracyof image detection of crack welding defects of large wind turbine impellers，a detection method based on the improved YOLOv5model was proposed.Based on the YOLOv5 model，this method introduced the CBAMatention mechanism after the backbone network of the YOLOv5 model，CSPDarknet53，to enhance the learning of important features，and directlycalculated the diference between the widthand heightof the predictionboxandthereal boxto replace the distance lossof theaspectratio，soas toavoid the failure of the model to converge，and realized the improvementof the YOLOv5 model.Finaly，the improved YOLOv5 model was used to detect thecrack welding defect image ofthe impellerof the large wind turbine，and the detection accuracyof the crack welding defect image of the impellerof the large wind turbine was improved.The simulation results showed that theaverage accuracy，precision，recalland F1valueof the proposed method for the detection of impeller crack welding defect images of large wind turbines reached 96.30% ， 96.77% ， 94.72% and 96.27% ，respectively， which had higher accuracyand faster detection speed of 22.38 frames/s compared with the standard YOLOv5 model，CNN model，SSD model and RESNET50 model.

Key Words ： wind turbines ;impeller cracks ; welding defects ;image detection ; YOLOv5 model

大型风电机组是风能发电的关键设备，对风能发电效率和运行状态具有重要意义。（剩余6131字）