Kang Ge
Papers
1
Total Citations
4
H-Index
1
About
Kang Ge is a researcher at the forefront of integrating artificial intelligence with industrial automation, with a primary focus on computer vision and intelligent robotics. His work centers on developing efficient, real-time object detection and segmentation algorithms tailored for complex manufacturing and textile applications. Ge’s most notable contribution is the design of a lightweight instance segmentation system for garment processing, leveraging the YOLOv11 network to enable robots to perform intricate tie-dye tasks with unprecedented speed and accuracy. This innovation, detailed in his 2025 paper “Efficient real-time instance segmentation of garment for intelligent robot tie-dye based on you only look once version 11 network,” has already garnered 4 citations, signaling early impact in the niche but growing field of AI-driven textile automation. By optimizing deep learning models for edge deployment, Ge addresses critical challenges in real-time visual perception for robotic manipulation, bridging the gap between theoretical computer vision and practical manufacturing needs. His work holds promise for revolutionizing traditional crafts through smart automation, making him a rising voice in intelligent manufacturing research.
Research Focus
Key Achievements
Top Papers
- 1