Qingshan Mal
Papers
1
Total Citations
2
H-Index
1
About
Qingshan Ma is a researcher whose work lies at the intersection of computer vision and intelligent power systems, with a particular focus on enhancing substation automation. His most cited paper, "Power Cabinet Door-opening State Recognition Technology Based on Edge Feature Extraction of Monocular Vision" (2019), addresses a critical operational challenge in smart substations: using machine vision to automatically detect whether power cabinet doors are open or closed. By developing a method that extracts edge features from monocular camera images, Ma enables patrol robots to perform this inspection task reliably, reducing the need for manual checks and improving safety. While his citation count is modest—with his top paper garnering 2 citations—this work represents a practical contribution to the growing field of robotic inspection in energy infrastructure. Ma’s research demonstrates how classical computer vision techniques can be adapted to solve real-world industrial problems, bridging the gap between laboratory algorithms and field deployment. For students and researchers exploring the application of vision systems in power grids, Ma’s work offers a clear example of how edge detection can be leveraged for state recognition in constrained environments.
Research Focus
Key Achievements
Top Papers
- 1