Yanling Han

Ministry of Agriculture and Rural Affairs, Shanghai Ocean University, Kyushu University

Papers

Total Citations

H-Index

About

Yanling Han is a researcher whose work lies at the intersection of computer vision, robotics, and marine technology, with a particular focus on intelligent underwater systems. Her most cited paper, "YOLOv6-ESG: A Lightweight Seafood Detection Method" (2023, 19 citations), introduces an efficient deep learning approach for automated seafood detection in complex underwater environments, addressing the critical need for robust object detection in automated fishing operations. This work demonstrates her ability to adapt state-of-the-art convolutional neural networks for practical marine applications. Han's earlier research further showcases her breadth in autonomous systems, including "Automatic path planning and navigation with stereo cameras" (2014, 4 citations), which tackles vehicle navigation in GPS-denied environments using stereo vision, and "Motions obtaining of multi-degree-freedom underwater robot by using reinforcement learning algorithms" (2010, 2 citations), where she applied natural gradient Actor-Critic algorithms for robotic arm motion planning. Collectively, her contributions span lightweight deep learning models, stereo-based navigation, and reinforcement learning for underwater robotics, establishing her as a researcher dedicated to advancing autonomous marine technologies.