Yulong Tao
Papers
2
Total Citations
122
H-Index
2
About
Yulong Tao is a leading researcher in underwater robotics and marine vision, with a focus on enabling autonomous object detection and manipulation in challenging open-sea environments. His major contributions center on the creation of the UDD (Underwater Open-sea Farm Object Detection Dataset), the first 4K HD dataset collected in real open-sea farms, comprising 2,227 images of sea cucumbers, sea urchins, and scallops. This foundational work, published in 2020 (13 citations), directly addresses the lack of realistic training data for underwater robots. Building on this, Tao introduced the Poisson GAN and AquaNet framework in 2021 (109 citations), a novel approach that significantly boosts underwater object grabbing capabilities for open-sea farming applications. His work has become a key reference for researchers developing autonomous picking systems in aquaculture, bridging the gap between computer vision and practical marine robotics. By providing high-quality, real-world datasets and innovative generative models, Tao has established himself as a pivotal figure in advancing underwater robot autonomy for sustainable seafood harvesting.
Research Focus
Key Achievements
Top Papers
- 1A New Dataset, Poisson GAN and AquaNet for Underwater Object Grabbing109 citations · 2021
- 2