Papers
3
Total Citations
32
H-Index
2
About
Xinting Yang is a researcher at the intersection of computer vision, aquaculture engineering, and autonomous robotics. Her work focuses on developing intelligent sensing and detection systems for challenging real-world environments, with a particular emphasis on underwater and agricultural applications. Yang’s most cited paper, "Three-dimensional location of target fish by monocular infrared imaging sensor based on a L–z correlation model" (2017, 20 citations), introduced a novel method for precise 3D fish localization using a single camera—a significant contribution to precision aquaculture. Building on this, she developed DF-DETR (2024, 10 citations), a transformer-based deep learning model specifically designed for dead fish detection in recirculating aquaculture systems, addressing a critical need for automated health monitoring in fish farming. Most recently, Yang has advanced the field of embedded robotics with her work on "Efficient and Hardware-Friendly Online Adaptation for Deep Stereo Depth Estimation on Embedded Robots" (2025), which enables accurate, real-time 3D perception on resource-constrained platforms like autonomous aerial vehicles. Her research demonstrates a consistent focus on deploying sophisticated computer vision algorithms in practical, hardware-limited settings, bridging the gap between state-of-the-art deep learning and real-world deployment in agriculture and robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2DF-DETR: Dead fish-detection transformer in recirculating aquaculture system10 citations · 2024
- 3