Yeming Liu

Tianjin University of Technology and Education

Papers

Total Citations

H-Index

About

Yeming Liu is a researcher advancing the field of intelligent waste management through computer vision and deep learning. His primary research areas include object detection, garbage classification, and environmental automation. Liu’s most notable contribution is his work on the “Garbage detection and classification method based on YoloV5 algorithm” (2022), which has garnered 6 citations. In this study, he tackled the significant challenge of automatically detecting and sorting diverse and voluminous waste by adapting the YOLOv5 algorithm for rapid, real-time classification. Training his model on the TACO dataset, Liu demonstrated a practical, high-efficiency solution for automating waste sorting—a critical step toward smarter recycling systems and reduced environmental impact. His work bridges artificial intelligence and sustainability, offering a scalable approach to one of modern society’s pressing logistical problems. By focusing on lightweight yet accurate detection, Liu’s research holds promise for deployment in smart bins, recycling facilities, and urban cleanup initiatives. His contributions highlight the potential of AI to transform waste management from a manual, labor-intensive task into an automated, data-driven process.