Shucheng Li
Papers
1
Total Citations
2
H-Index
1
About
Shucheng Li is a researcher at the forefront of underwater computer vision, specializing in the development of high-precision, real-time object detection models for challenging marine environments. His primary research areas include deep learning architectures, dynamic adaptive algorithms, and underwater image processing. Li’s most notable contribution is the creation of DyAqua-YOLO, a state-of-the-art object detection model that dynamically adapts to the unique visual distortions of turbid, low-illumination, and spectrally limited underwater scenes. This breakthrough directly addresses the critical need for reliable target perception in underwater robot inspection and marine resource exploitation. Although recently published in 2025, his work has already garnered 2 citations, signaling strong early impact in the field. By integrating dynamic adaptive architecture into the YOLO framework, Li has significantly enhanced both the accuracy and speed of underwater detection, paving the way for more autonomous and efficient underwater operations. His innovative approach holds promise for advancing marine robotics, environmental monitoring, and offshore infrastructure maintenance.
Research Focus
Key Achievements
Top Papers
- 1