Concrete wind turbine tower crack assessment based on drone imaging using computer vision and artificial intelligence

Abstract

Wind energy is a sustainable and renewable energy source, valued and invested in by national governments. Wind power currently has significant installed capacity and growth, and wind turbines will face substantial maintenance demands in the future. However, inspection and assessment of wind turbine towers lack sufficient research attention and effective automated methods. Fine cracks in large-scale images of wind turbine towers are often obscured by noise, making accurate identification difficult. The projection of the same crack in adjacent images is susceptible to double counting, leading to incorrect evaluations. This paper proposes a crack assessment method for concrete wind turbine towers based on unmanned aerial vehicle (UAV) imaging, utilizing computer vision and artificial intelligence (AI). The flight trajectory and photographic strategy for high-rise structures are designed for data acquisition. An attention-enhanced grid-based convolutional neural network (CNN) for crack identification is developed, along with an incremental crack projection algorithm to address overlapping regions in adjacent images. Field tests demonstrate that the proposed method achieves excellent accuracy and efficiency in crack identification, localization, and quantification. The image acquisition strategy ensures reliable crack identification and three-dimensional reconstruction. The classification model, optimized by the global receptive field and attention mechanism, combined with digital image processing (DIP) adapted to grid-based classification, effectively filters noise and extracts fine cracks. Three-dimensional reconstruction and incremental projection based on structure from motion (SfM) provide accurate and efficient crack localization, allowing precise crack parameters to be obtained from the surface model for structural assessment. The proposed method integrates drone imaging, AI-based crack recognition algorithms, and three-dimensional reconstruction and projection techniques, effectively enabling automatic crack assessment for concrete wind turbine towers, thereby assisting more informed maintenance decisions.