Applying Segment Anything Model to Ground-Based Video Surveillance for Identifying Aquatic Plant

Water hyacinth (Eichhornia crassipes), with its rapid growth and reproductive capacities, poses a formidable challenge to aquatic ecosystems worldwide. Traditional satellite remote sensing, while effective for large-scale monitoring, incurs high costs and limited applicability for localized surveillance. Unmanned aerial vehicle (UAV) offers higher spatial resolution but is hampered by operational complexity, deployment costs, and weather-dependent limitations, preventing continuous monitoring. This study capitalizes on the cost-effectiveness and real-time capabilities of network surveillance cameras for persistent observation, assembling a dataset from water hyacinth imagery captured in waterways in Shanghai. We developed a recognition and segmentation model tailored for water hyacinth by integrating the Segment Anything Model with the YOLOv8 algorithm. Complementary to ground-based data acquisition, UAV photogrammetry was utilized to establish a perspective transformation matrix, enabling accurate quantification of the water hyacinth’s spread. Our approach demonstrates a scalable and cost-effective solution with potential applicability in continuous aquatic plant management.