Shang-En Shen
Papers
1
Total Citations
5
H-Index
1
About
Shang-En Shen is a researcher at the forefront of integrating deep learning with autonomous systems, with a primary focus on computer vision for aviation and robotic control. His most cited work introduces a groundbreaking YOLO-based deep learning framework for needle-type dashboard recognition, enabling a fully automatic auxiliary flying system for autopilot maneuvering. By developing a control vision system capable of reading diverse analog meters, Shen’s modified object detection model precisely interprets airspeed readings, directly advancing the feasibility of robot-piloted flight. This contribution, with 5 citations, demonstrates his ability to bridge real-time visual perception and autonomous decision-making in high-stakes environments. Shen’s research holds significant promise for enhancing safety and efficiency in aviation, as well as broader applications in industrial automation where legacy analog instruments remain prevalent. His work exemplifies the practical deployment of state-of-the-art AI, offering a scalable solution for modernizing cockpit systems and robotic navigation.
Research Focus
Key Achievements
Top Papers
- 1