Abstract: The safety of wind turbine tower,as the support structure of the whole wind turbine,is crucial.As one of the main diseases of wind turbine tower,it is necessary to detect cracks accurately.Due to the lack of feature extraction capability,the existing crack detection algorithms have low accuracy and high model complexity,which cannot well meet the needs of end-side equipment on-site detection.For this reason,this paper proposes a YOLO-based wind tower safety detection algorithm C2P-YOLO.In the backbone network part,the algorithm utilizes the lightweight feature extraction module C2P instead of the redundant network structure,in order to extract richer feature information in the feature map.In the neck network part,the algorithm adds the lightweight up-sampling CARFE and attention mechanism modules to complement the information loss in the feature fusion process.Experimental results show that the algorithm achieves a mAP score of 84.9% on the publicly available dataset NEU-DET,which is 3%~8% higher than similar algorithms,and it can maintain a better lightweight property.

Key words: Deep learning, Defect detection, Object detection