Dongyan Huang
Papers
2
Total Citations
4
H-Index
2
About
Dongyan Huang is a leading researcher in artificial intelligence, with a primary focus on efficient deep learning architectures for real-world applications, particularly in robotics and agriculture. Her work bridges the gap between advanced computer vision models and resource-constrained edge devices, enabling intelligent automation in complex environments. A key contribution is the development of FSA-DETR-P, a lightweight detection framework that optimizes the RT-DETR architecture with an efficiency-oriented backbone and adaptive scale fusion. This system achieves high-performance pomegranate detection under challenging orchard conditions—including variable lighting, occlusion, and scale changes—demonstrating its potential for automated harvesting on edge devices. Huang also played a pivotal role in organizing the IEEE SLT 2021 Alpha-mini Speech Challenge, providing open datasets, tracks, and baselines to advance keyword spotting and sound source localization on humanoid robots. Her work in this challenge has catalyzed improvements in deep learning-based speech processing for robotics. With her papers already garnering citations, Huang’s research is shaping the future of efficient AI, making sophisticated perception systems accessible for practical, resource-limited deployments.
Research Focus
Key Achievements
Top Papers
- 1
- 2