Surface Defect Detection of Wind Turbines Based on Improved YOLOv7 Algorithm
WANG Zhi
GAO Lin
YANG Yu
Abstract：Directing at the problems that the surface defects of wind turbines involve various types,significant differences in scale and difficulty in feature extraction,etc.,an improved YOLOv7(you only look once version 7)algorithm was proposed for wind turbine surface defect detection.Firstly,the asymptotic feature pyramid network(AFPN)was used to replace the path aggregation feature pyramid network(PAFPN)in the YOLOv7 neck network,which solved the problem of feature loss and degradation in the multi-scale fusion process and reduced the model complexity.Secondly,the efficient layer aggregation network-wide(ELAN-W)module was used to replace the introductory module in AFPN,which improved the feature extraction capability of the model.Finally,convolution and spatial group-wise enhance(SGE)attention mechanisms were used to build a convolutional attention module to improve the model's positioning ability and detection performance of detection targets.The experimental results showed that the mean average precision and detection speed of the improved YOLOv7 algorithm for surface defect detection of wind turbines reached 85.4%and 133.0 frames/s,respectively,which were improved by 1.8%and 17.7%compared to the original YOLOv7 algorithm.The research results effectively improved the surface defect detection performance of wind turbines.
Keywords：wind turbinesYOLOv7AFPNELAN-WSGEinspectionmulti-scale fusion
Publication Date：2024-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 75-80 )