A New Coral Classification Method Using Speed Sea Scanner-Portable and Deep Learning-Based Point Cloud Semantic Segmentation

Traditional coral classification methods based on underwater images have many limitations in coral surveys, such as light attenuation, color distortion, and complex backgrounds, which affect their accuracy and reliability. To overcome these challenges, we propose a new coral classification method using Speed Sea Scanner-portable (SSS-P-P) and deep learning-based point cloud semantic segmentation. SSS- P can efficiently acquire high-resolution coral images, and we further build coral point clouds through Structure from Motion (SfM) technology. The point cloud semantic segmentation technology can accurately identify and classify corals and other seafloor features. Experimental results show that this method significantly outperforms traditional image segmentation techniques in coral classification accuracy and detection efficiency, providing strong technical support for the research and protection of coral ecosystems.