Vision-Based Autonomous Target Recognition and Tracking for Unmanned Underwater Vehicles (UUVs)

Vision-based autonomous target recognition and tracking are crucial capabilities for Unmanned Underwater Vehicles (UUVs) to operate effectively in complex marine environments. This article delves into the latest advancements in this field, examining the challenges and opportunities presented by underwater vision. We discuss various algorithms employed for target recognition, including deep learning techniques and feature-based approaches. Furthermore, we explore tracking algorithms that enable UUVs to maintain continuous awareness of moving targets. The article also highlights the challenges posed by the underwater environment, such as poor visibility, light attenuation, and dynamic backgrounds. Finally, we outline future research directions, emphasizing the potential of advanced computer vision and artificial intelligence to enhance UUV autonomy and performance in challenging underwater missions.