Biofouling Detection and Extent Classification in Tidal Stream Turbines via a Soft Voting Ensemble Transfer Learning Approach

This paper investigates the use of transfer learning models for detecting and classifying biofouling extent in tidal stream turbines. It provides a comprehensive exploration of a soft voting ensemble approach to deal with a multiclass image detection problem. In this context, three well-known convolutional neural network models, namely VGG16, ResNet50, and MobileNetV2, are the primary focus of the proposed study and are trained on an experimental dataset issued from the Shanghai Maritime University tidal stream turbine platform.