Fast R-CNN-based Detection and Coverage Estimation of Biofouling on Tidal Stream Turbines

Biofouling poses a significant challenge to the efficiency and performance of tidal stream turbines. This study proposes a novel approach utilizing Fast R-CNN for biofouling detection and coverage estimation in tidal stream turbines. We use a real biofouling dataset collected from four turbines installed on the pier of Ikitsuki Bridge in Hirado, Nagasaki, Japan, over 8 months, containing both clean and biofouled turbine images. Employing various augmentation techniques, we enhanced the dataset’s robustness. Our method achieved a remarkable 94% accuracy in detecting and classifying biofouled turbine blades. Additionally, using the OpenCV library in Python, we estimated the extent of biofouling coverage on turbine blades, offering valuable insights for maintenance and optimization efforts and assisting informed decision-making on strategies for tidal stream turbine maintenance and performance optimization.