AQUADA-UNI: A super-efficient unified cascade multimodal model for thermographic wind turbine blade segmentation and damage detection

Structural damage inspection of wind turbine rotor blades is crucial for preventing catastrophic failures. AI-driven automated approaches are increasingly being used in blade damage inspection due to their high efficiency and consistency. This paper proposes a novel AI-driven cascade multimodal model to perform thermographic blade segmentation and blade damage detection in one single step with exceptional computational efficiency. For blade segmentation, this model leverages RGB images to enhance thermographic blade segmentation, enabling multimodal complementarity and improving segmentation accuracy. For blade damage detection, the model employs hard example mining and curriculum learning to significantly enhance its ability to distinguish anomalous boundaries between normal and anomaly image patches. Utilizing a computationally efficient cascade network architecture, this model unifies conventional multi-step tasks into one single step for AI-based blade damage detection. Compared to the state-of-the-art method (i.e., the second-best method in our benchmark study), the proposed model halves the computational cost and nearly triples the Frames Per Second (FPS) while maintaining equivalent or slightly higher prediction accuracy, particularly in the test cases where the blade damage is small and its thermal contrast is low, making analysis with conventional AI-based methods extremely challenging. We also shared the dataset with the public to support further study.