Lin Meng
Papers
13
Total Citations
220
H-Index
7
About
Lin Meng is a leading researcher at the intersection of robotics, deep learning, and edge computing, with a primary focus on developing intelligent systems to address critical labor shortages caused by aging populations worldwide. His most significant contributions center on empty-dish recycling robots, where he has pioneered a series of lightweight, real-time object detection algorithms—including YOLO-GD, YOLO-MSA, and YOLO-GG—that enable robots to autonomously detect, grasp, and recycle dishes with high accuracy on resource-constrained edge devices. His work has garnered substantial attention, with his top-cited paper accumulating 64 citations, and several others exceeding 20 citations. Beyond tableware recycling, Meng has innovated in gait prediction for lower limb exoskeletons using transformer-based neural networks and plantar force data, demonstrating the versatility of his deep learning expertise. He has also explored FPGA acceleration for on-robot AI inference and cross-disciplinary applications, including preserving ancient cultural heritage. Meng’s research is notable for its practical impact, directly translating algorithmic advances into deployable robotic solutions that enhance productivity in the food service industry and assistive technologies for mobility-impaired individuals.
Research Focus
Key Achievements
Top Papers
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- 2An Ultralightweight Object Detection Network for Empty-Dish Recycling Robots36 citations · 2023
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- 7Research on Deep Learning-based Cross-disciplinary Applications7 citations · 2022
- 8YOLO-GG: a slight object detection model for empty-dish recycling robot6 citations · 2022
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