Zeyu Jiao

Guangdong Institute of Intelligent Manufacturing

Papers

Total Citations

H-Index

About

Zeyu Jiao is a researcher whose work sits at the intersection of computer vision, human-computer interaction, and sustainable AI applications. His primary research focuses on developing efficient object detection and gesture recognition systems using advanced neural network architectures, particularly variants of the YOLO (You Only Look Once) family. Jiao’s major contributions include a gesture recognition method leveraging the YOLOv4 network, designed to enhance interaction in collaborative robotics and smart home environments by addressing challenges like gesture similarity and occlusion. He has also made notable strides in applied AI for environmental sustainability, proposing a YOLOv3-SPP model pruning approach for municipal solid waste classification—a solution aimed at reducing labor costs and health risks associated with traditional manual sorting. While his most-cited works have garnered initial attention with 2 citations each, they represent foundational steps toward practical, real-world deployment of lightweight vision models. Jiao’s work is particularly relevant for researchers exploring the intersection of model efficiency, human-robot collaboration, and eco-friendly technology, marking him as an emerging voice in applied deep learning for societal benefit.