Xiaojing Dang
Papers
1
Total Citations
32
H-Index
1
About
Xiaojing Dang is a leading researcher in computer vision and deep learning, with a focused expertise in industrial automation and intelligent inspection systems. Her most impactful work, "A Pointer Meter Recognition Algorithm Based on Deep Learning" (2020, 32 citations), addresses a critical challenge in smart substation management: the accurate automatic reading of analog pointer meters by inspection robots. This contribution is pivotal for transitioning from manual to unmanned operations, directly improving recognition accuracy—a persistent bottleneck in the field. Dang’s research bridges the gap between theoretical deep learning models and practical deployment in high-stakes environments like power infrastructure. By enhancing the reliability of automated visual inspection, her work supports the broader goals of intelligent, self-managed substations, reducing human error and operational costs. With 32 citations, this paper has already influenced subsequent studies in industrial anomaly detection and robotic perception. Dang’s achievements underscore her role in advancing real-world AI applications, making her a key figure in the evolution of smart grid technology and autonomous monitoring systems.
Research Focus
Key Achievements
Top Papers
- 1A Pointer Meter Recognition Algorithm Based on Deep Learning32 citations · 2020