Papers
1
Total Citations
54
H-Index
1
About
Lulu Fan is a leading researcher in the fields of construction waste management and computer vision, with a particular focus on the automated detection and classification of construction and demolition (C&D) debris. Her most influential work, "RGB-D fusion models for construction and demolition waste detection" (2021), has garnered 54 citations, establishing a foundational approach for integrating color and depth data to improve waste sorting accuracy. Fan’s major contribution lies in developing sensor fusion techniques that enhance the reliability of robotic and vision-based systems for recycling, directly addressing the inefficiencies in manual waste processing. By combining RGB and depth information, her models enable more precise identification of mixed waste materials, a critical step toward sustainable construction practices. Her research bridges the gap between artificial intelligence and environmental engineering, offering scalable solutions for smart waste management. Fan’s work is particularly notable for its practical impact, influencing both academic studies and industrial applications in automated recycling. Her contributions continue to shape how researchers and engineers approach the challenge of reducing landfill waste through intelligent detection systems.
Research Focus
Key Achievements
Top Papers
- 1RGB-D fusion models for construction and demolition waste detection54 citations · 2021