Meng Huang
Papers
1
Total Citations
37
H-Index
1
About
Meng Huang is a researcher at the forefront of applying deep learning to environmental monitoring, with a particular focus on aquaculture systems. Their most cited work, "Multi-target tracking algorithm in aquaculture monitoring based on deep learning" (2023), has garnered 37 citations, demonstrating a growing impact in the field. Huang's primary research areas include computer vision, object tracking, and the integration of artificial intelligence into ecological and agricultural monitoring. Their major contribution lies in developing robust algorithms capable of simultaneously tracking multiple aquatic organisms in complex, real-world underwater environments—a challenge that has significant implications for sustainable fish farming, resource management, and automated surveillance. By leveraging deep learning architectures, Huang has advanced the precision and efficiency of non-invasive monitoring, reducing reliance on manual observation. This work not only addresses critical bottlenecks in aquaculture but also provides a scalable framework for broader environmental sensing applications. As a rising voice in applied AI, Huang’s research bridges the gap between theoretical computer science and practical, high-impact ecological solutions.
Research Focus
Key Achievements
Top Papers
- 1