Deep Learning-based Detection of Deep-sea Hydrothermal Polymetallic Sulfides, Plume, and Their Biological Communities

Because of their rich mineral resources and extreme ecological environments, deep-sea hydrothermal vent areas are significantly valuable both scientifically and economically. Optical imaging is the most direct method for verifying deep-sea hydrothermal activity. Autonomous detection and recognition of hydrothermal activity using optical imaging can significantly improve underwater robot detection efficiency. However, because of the difficulty in obtaining optical images of deep-sea hydrothermal activity and the limited accumulation of data, to date, there have been few studies on the autonomous detection of various characteristic targets of hydrothermal activity. To achieve autonomous detection and recognition of deep-sea hydrothermal polymetallic sulfides, plumes, and their biological communities, this study developed an optical image dataset of deep-sea hydrothermal activity, proposed an improved YOLOv8-based multi-target real-time detection algorithm, introduced the CBAM attention mechanism, and modified the network structure. Experimental results show that the improved algorithm achieves better target detection performance, and the response time reaches 6.8ms, which satisfies the real-time performance for underwater robots to autonomously identify hydrothermal vents.