Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 250100126-6.doi: 10.11896/jsjkx.250100126

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

C2P-YOLO:A Lightweight Crack Detection Algorithm for Wind Turbine Towers

DUAN Pengsong1, GAO Yang1, ZHANG Dalong1, CAO Yangjie1, ZHAO Jie2   

  1. 1 School of Cyberspace Security,Zhengzhou University,Zhengzhou 450000,China
    2 Shanghai Red Sandalwood Intelligent Technology Co.,Ltd.,Shanghai 200000,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:DUAN Pengsong,born in 1983,Ph.D,associate professor.His main research interests include edge computing and intelligent perception.
    CAO Yangjie,born in 1976,professor.His main research interests include machine learning,computer vision and high-performance computing.
  • Supported by:
    Zhengzhou City Collaborative Innovation Major Project(20XTZX06013),Strategic Consulting Research Project of Henan Research Institute of China Engineering Science and Technology Development Strategy(2022HENYB03) and Science and Technology Tackling Project of Henan Province(232102210050,242102210060).

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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

CLC Number: 

  • TP183
[1]SHAN L.Selection of the wind turbine tower structure and mechanical properties research[D].Harbin:Harbin Institute of Technology,2009:12-15.
[2]HAN Q,ZHANG Z,XU X Y,et al.Steel surface defect detection algorithm based on FF R-CNN[J].Journal of Taiyuan University of Technology,2021,52(5):754-763.
[3]SI S L.Light weight YOLOv4 steel surface defect detectionmethod[D].Fuxin:Liaoning University of Engineering and Technology,2022.
[4]DING S,YANG C,ZHANG S.Acoustic-Signal-Based Damage Detection of Wind Turbine Blades－A Review[J].Sensors,2023,23(11):4987.
[5]ZHANG Y,YANG Y,SUN J,et al.Surface Defect Detection of Wind Turbine Based on Lightweight YOLOv5s Model[J].Measurement,2023:113222.
[6]GENG R S,SHEN G T,LIU S F.Acoustic emission signal processing and analysis technology[J].Nondestructive Testing,2002,24(1):23-28.
[7]XU D,LIU P F,CHEN Z P.Damage mode identification andsingular signal detection of composite wind turbine blade using acoustic emission[J].Composite Structures,2021,255:112954.
[8]ZHAO Z,CHEN N Z.Acoustic emission based damage source localization for structural digital twin of wind turbine blades[J].Ocean Engineering,2022.
[9]BEJGER A,DRZEWIENIECKI J B,BARTOSZKO P,et al.The Use of Coherence Functions of Acoustic Emission Signals as a Method for Diagnosing Wind Turbine Blades[J].Energies,2023,16(22):7474.
[10]LI J,SU Z,GENG J,et al.Real-time detection of steel strip surface defects based on improved YOLO detection network[J].IFAC-Papers On Line,2018,51:76-81.
[11]ZHANG C,CHANG C,JAMSHIDI M.Concrete bridge surface damage detection using a single-stage detector[J].Comput-Aided Civil Infrastruct Eng,2020,35:389-409.
[12]LIU Q,WANG C,LI Y,et al.A Fabric Defect Detection Method Based on Deep Learning [J].IEEE Access,2022,10:4284-96.
[13]OLIVIER R,HANQIANG C.Nearest Neighbor Value Interpolation[J].International Journal of Advanced Computer Science &Application,2012,3(4):25-30.
[14]WANG J,CHEN K,XU R,et al.Carafe:Content-aware reas-sembly of features[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:3007-3016.
[15]LIN H,JING C,HUANG Y,et al.A2 Net:Adjacent Aggregation Networks for Image Raindrop Removal[J].IEEE Access,2020,PP(99):1.
[16]SONG K,YAN Y.A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects[J].Applied Surface Science,2013,285:858-864.
[17]LV X,DUAN F,JIANG J,et al.Deep metallic surface defect detection:The new benchmark and detection network[J].Sensors,2020,20(6):1562.
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