Multi-View and Multi-Type Feature Fusion Rotor Biofouling Recognition Method for Tidal Stream Turbine

Power generation is affected and structural instability may occur when biofouling attaches to the rotor of tidal stream turbines (TSTs). Image signals are used to identify biofouling for biofouling recognition, thus achieving on-demand maintenance, optimizing power generation efficiency, and minimizing maintenance costs. However, image signals are sensitive to background interferences, and underwater targets blend with the water background, making it difficult to extract target features. Changes in water turbidity can affect the effectiveness of image signal biofouling recognition, which can lead to reduced recognition accuracy. In order to solve these problems, a multi-view and multi-type feature fusion (MVTFF) method is proposed to recognize rotor biofouling on TSTs for applications in TST operation and maintenance. (1) Key boundary and semantic information are captured to solve the problem of background feature interference by comparing and fusing the extracted multi-view features. (2) The local geometric description and dependency are obtained by integrating contour features into multi-view features to address the issue of the target mixing with water. The mIoU, mPA, Precision, and Recall of the experimental results show that the method achieves superior recognition performance on TST datasets with different turbidity levels.