Lingling Zhou

China University of Mining and Technology

Papers

Total Citations

H-Index

About

Lingling Zhou is a leading researcher in intelligent visual perception and autonomous robotic systems, with a particular focus on challenging underground environments. Her work bridges computer vision, image processing, and mining automation, addressing the critical need for robust feature recognition in low-light, dusty, and unstructured settings. Zhou’s most cited paper, “Edge detection based on Retinex theory and wavelet multiscale product for mine images” (2016, 21 citations), introduces a pioneering method that combines Retinex illumination correction with wavelet-based multiscale analysis to enhance edge detection in coal mine images. This contribution is foundational for enabling mine robots to perceive complex surroundings and navigate autonomously, directly supporting the push toward automated, safer mining operations. Her research has been instrumental in advancing real-time video analysis and environmental sensing for industrial robotics, with her work cited by peers developing similar vision systems for extreme conditions. Zhou’s achievements highlight her as a key innovator at the intersection of computer vision and mining technology, making her a vital figure for students and researchers interested in applied perception systems for hazardous environments.