Huanlong Liu
Papers
5
Total Citations
33
H-Index
3
About
Huanlong Liu is a leading researcher in intelligent robotics and computer vision, with a focused expertise in developing advanced object detection and visual servoing systems for industrial automation. His primary research areas include lightweight deep learning models, image-based visual servoing (IBVS), and robotic grasping in complex, cluttered environments. Liu’s major contributions center on solving the challenging problem of automatic positioning and grasping of bolster springs and wedges in railway wagon assembly, where traditional methods struggle with geometric complexity and background noise. He pioneered the integration of YOLO-based object detection with visual servoing control, proposing novel models such as MGBM-YOLO and M-G-YOLOv5s that dramatically improve both speed and accuracy for real-time robotic manipulation. His work on Pruned-Ghost-YOLOv3 further advanced lightweight positioning for wedge support robots. With over 30 citations across his most-cited papers, Liu’s research has direct impact on intelligent dismantling equipment and smart manufacturing. Notably, his 2022 MGBM-YOLO paper (17 citations) represents a breakthrough in faster, lighter object detection for robotic grasping, establishing him as a key innovator at the intersection of computer vision and industrial robotics.
Research Focus
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Top Papers
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