Xuelian Sun

Papers

Total Citations

H-Index

About

Xuelian Sun is a researcher advancing the field of underwater computer vision, with a primary focus on deep learning-based object detection for marine and aquatic environments. Her most notable contribution is the development of the MSD-YOLOv5 algorithm, a specialized adaptation of the YOLOv5 framework designed to overcome the unique challenges of underwater imaging, including poor visibility, color distortion, and complex backgrounds. This work, published in 2023, directly addresses critical needs in oceanography, such as enabling underwater robots to map the seabed, detecting vessels and personnel at sea, and empowering aquaculture farmers to monitor aquatic species distribution and adjust stocking densities for optimal yield. While her citation count is currently emerging, the practical significance of her algorithm positions it as a foundational tool for autonomous underwater systems and precision aquaculture. Sun’s research sits at the intersection of artificial intelligence and marine science, offering scalable solutions for environmental monitoring and resource management. Her work exemplifies how tailored deep learning models can transform challenging real-world domains, making her a rising voice in applied computer vision.