Papers
1
Total Citations
11
H-Index
1
About
Qunbiao Wu has made significant contributions to the field of construction waste management through the application of advanced deep learning and computer vision techniques. His research focuses on the real-time detection and classification of impurities in construction and demolition waste, a critical challenge for sustainable urban development and recycling efficiency. Wu’s most-cited work, "Real-time detection of construction and demolition waste impurities using the improved YOLO-V7 network" (2024), has already garnered 11 citations, demonstrating its immediate impact on the research community. In this study, he enhanced the YOLO-V7 architecture to achieve high-speed, accurate identification of contaminants in waste streams, offering a practical solution for automated sorting systems. This innovation not only advances the state of the art in waste analytics but also provides a scalable tool for reducing landfill burden and promoting circular economy practices. Wu’s work stands out for its direct applicability to industrial settings, bridging the gap between cutting-edge AI and environmental engineering. His research continues to inspire further developments in intelligent waste management, making him a notable emerging voice in sustainable technology.
Research Focus
Key Achievements
Top Papers
- 1