Ruishu Xu

Guilin University of Electronic Technology

Papers

Total Citations

H-Index

About

Ruishu Xu is a leading researcher in underwater robotics and computer vision, with a primary focus on advancing deep learning techniques for challenging aquatic environments. Their most cited work, "Underwater Robot Target Detection Based On Improved YOLOv5 Network" (2024, 2 citations), addresses critical limitations in applying terrestrial object detection models to underwater settings. Xu’s major contribution lies in enhancing the YOLOv5 architecture to overcome obstacles such as complex backgrounds, degraded image quality, and small, clustered targets, while maintaining efficiency under constrained computational resources. This innovation directly improves the autonomy and reliability of underwater robots for tasks like environmental monitoring and marine exploration. Although early in its citation trajectory, this paper signals Xu’s growing influence in bridging the gap between state-of-the-art AI and real-world underwater applications. Their work is notable for its practical engineering focus, offering robust solutions that balance accuracy and speed—a vital requirement for resource-limited underwater platforms. For students and researchers, Xu’s research exemplifies how targeted algorithmic improvements can unlock new capabilities in niche, high-impact domains.