Zicong Yang
Papers
1
Total Citations
3
H-Index
1
About
Zicong Yang is an emerging researcher in artificial intelligence and computer vision, with a focused interest in applying deep learning to environmental sustainability. His primary research area centers on intelligent waste management systems, particularly the development of enhanced object detection algorithms for garbage identification and classification. Yang's most notable contribution is his work on optimizing the YOLOv5 architecture for real-time waste sorting, as detailed in his 2024 paper "Enhanced and improved garbage identification and classification of YOLOV5 based on data." This research addresses the pressing global challenge of efficient waste segregation by proposing a convolutional neural network-based detection model tailored for garbage sorting robots. By improving the accuracy and speed of identifying different waste categories, Yang's work directly supports the development of smarter, automated recycling systems. His research has already garnered 3 citations, signaling growing interest from the computer vision and environmental engineering communities. Yang's contributions are particularly timely, as they offer a scalable, AI-driven solution to the critical problem of municipal solid waste management, demonstrating how deep learning can be harnessed for tangible environmental benefits.
Research Focus
Key Achievements
Top Papers
- 1