Qingqing Sun

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Qingqing Sun is a leading researcher in computer vision and intelligent waste management, with a particular focus on deep learning-based object detection and tracking. Their most cited work, "DSYOLO-trash: An attention mechanism-integrated and object tracking algorithm for solid waste detection" (2024, 34 citations), introduces a novel framework that enhances the YOLO architecture with attention mechanisms to improve the accuracy and efficiency of detecting solid waste in complex environments. This contribution addresses critical challenges in automated waste sorting and environmental monitoring, offering a scalable solution for real-time detection and tracking. Sun's research bridges the gap between advanced AI techniques and practical sustainability applications, demonstrating significant impact in the field of smart city technologies. By integrating object tracking with detection, their work enables continuous monitoring of waste streams, reducing manual intervention and improving recycling rates. With growing citations reflecting its relevance, Sun's algorithm has become a reference point for researchers developing AI-driven environmental solutions. Their achievements underscore a commitment to leveraging cutting-edge computer vision for pressing global challenges, making them a notable figure in applied artificial intelligence for ecological sustainability.