Liwen Chen
Papers
1
Total Citations
4
H-Index
1
About
Liwen Chen is a researcher at the forefront of intelligent robotics and nuclear safety, specializing in the development of autonomous cleaning systems for hazardous environments. Their most notable contribution is the creation of S-YOLOv5s, a lightweight deep learning model that integrates ShuffleNetV2 with YOLOv5s for real-time dust detection on nuclear power plant (NPP) reactor containment walls. This innovation addresses a critical safety gap: wall-climbing cleaning robots previously operated blindly, unable to assess dust accumulation—a risk that can lead to radioactive dust formation, endangering both personnel and the environment. By enabling robots to detect and prioritize cleaning with high accuracy, Chen’s work directly enhances operational safety in nuclear facilities. With their 2024 paper already garnering 4 citations, the research is gaining traction for its practical impact. Chen’s contributions exemplify a blend of computer vision, robotics, and nuclear engineering, offering a scalable solution for contamination control. Their work not only advances autonomous maintenance in extreme environments but also sets a foundation for future studies in intelligent hazard mitigation.
Research Focus
Key Achievements
Top Papers
- 1