Siyuan Yuan

Guilin University of Electronic Technology

Papers

Total Citations

H-Index

About

Siyuan Yuan is a researcher at the forefront of underwater robotics and computer vision, with a primary focus on advancing deep learning-based object detection in challenging marine environments. Yuan’s most cited work introduces an improved YOLOv5 network specifically designed to overcome the unique obstacles of underwater target detection—such as complex backgrounds, poor image quality, and small, clustered objects—while operating under limited computational resources. This contribution addresses a critical gap where terrestrial detection models fail underwater, offering a more robust and efficient solution for autonomous underwater vehicles. Although early in their career, Yuan’s research has already garnered attention, with their flagship paper accumulating citations that underscore its relevance to the growing field of underwater robotics. By tackling the intersection of deep learning and marine exploration, Yuan is paving the way for more reliable autonomous systems in underwater inspection, environmental monitoring, and search-and-rescue operations. Their work represents a promising step toward bridging the gap between land-based AI advancements and the unique demands of the underwater domain.