Wanqi Ma

Papers

Total Citations

H-Index

About

Wanqi Ma is a rising researcher in the field of environmental artificial intelligence, specializing in deep learning-based solid waste detection and intelligent recycling systems. Ma’s work centers on integrating attention mechanisms and object tracking algorithms to enhance the accuracy and efficiency of waste identification in complex, real-world environments. Their most-cited paper, “DSYOLO-trash: An attention mechanism-integrated and object tracking algorithm for solid waste detection” (2024, 34 citations), introduces a novel adaptation of the YOLO framework that significantly improves detection of litter in cluttered scenes, a critical step toward automated waste management. Building on this, Ma’s more recent work, “An integrated detection-semantic fusion and near-infrared system for food-delivery packaging waste” (2025), pioneers a multi-sensor fusion approach combining visual and near-infrared data to tackle the growing challenge of takeout packaging pollution. Although early in their career, Ma’s contributions are already gaining recognition for their practical impact on sustainability and smart city initiatives. Their research bridges computer vision and environmental engineering, offering scalable solutions for waste sorting and recycling. With a clear trajectory toward high-impact, applied AI, Wanqi Ma is a promising voice in the fight against global waste.