Yixing Meng
Papers
2
Total Citations
11
H-Index
2
About
Yixing Meng is a researcher at the forefront of applying deep learning to industrial automation and environmental sustainability. His work centers on computer vision and intelligent robotic perception, with a particular focus on object detection and precise localization in complex real-world scenarios. Meng’s most impactful contribution is his study on the efficient recognition and accurate localization of waste plastic bottles using deep learning, which has garnered 8 citations. This research addresses a critical bottleneck in ecological recycling—enabling automated sorting based on color and material value, thereby enhancing resource recovery and reducing environmental harm. Additionally, his work on weld start point detection and localization (3 citations) demonstrates the versatility of his methods, extending deep learning techniques to precision manufacturing tasks. By bridging the gap between advanced AI and practical engineering challenges, Meng’s research offers scalable solutions for both green technology and industrial robotics, making him a notable emerging voice in applied deep learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2