Deep Learning-Based Image Recognition Technology for Wind Turbine Blade Surface Defects

this paper proposes WindDefectNet, an image recognition system for surface defects of wind turbine blades, aiming at solving the key problems in wind turbine blade maintenance. At the beginning of the system design, the functional requirements and performance index requirements are clarified to ensure the realization of the functions of image acquisition and preprocessing, defect detection and classification, defect localization and size measurement, and to emphasize the key performance indexes such as accuracy, recall, processing speed and robustness of the system. The system architecture consists of multiple modules, including image acquisition and preprocessing module, feature extraction module, attention enhancement module, defect detection module, etc., which work together to achieve efficient defect recognition and localization. By adopting advanced deep learning techniques and model design, WindDefectNet is able to maintain high accuracy and stability in complex environments. Experimental results show that WindDefectNet performs well under different lighting conditions, shooting angles, wind speed and weather conditions, and has good environmental adaptability and robustness. The system provides strong technical support for blade maintenance in the wind power industry.