Xichang Cai
Papers
1
Total Citations
3
H-Index
1
About
Xichang Cai is a researcher focused on industrial safety and intelligent monitoring, with key contributions in computer vision applications for infrastructure maintenance. His most-cited work, "Water leakage detection and its application of turbine floor equipment based on MaskRCNN" (2022, 3 citations), addresses a critical challenge in power plants: detecting water leaks under weak and uneven lighting conditions on turbine floors. By adapting the Mask R-CNN deep learning architecture, Cai developed a method that enhances detection accuracy in low-visibility industrial environments, directly reducing economic losses and safety hazards. This work demonstrates his expertise in combining deep learning with real-world engineering constraints, offering practical solutions for automated facility inspection. While his citation count is modest, the research holds significant potential for scaling in industrial IoT and predictive maintenance systems. Cai’s approach exemplifies how advanced computer vision can be tailored to challenging operational settings, making his work relevant for researchers and engineers working on safety-critical monitoring systems.
Research Focus
Key Achievements
Top Papers
- 1