Lu Xingqin
Papers
1
Total Citations
4
H-Index
1
About
Lu Xingqin is a researcher whose work lies at the intersection of computer vision, agricultural robotics, and image enhancement. Their key research area focuses on developing algorithms that improve machine perception in challenging, low-light environments—a critical need for autonomous systems operating in real-world agricultural settings. Xingqin’s most notable contribution is the "Edge-preserving Retinex enhancement algorithm of night vision image for apple harvesting robot," which addresses the fundamental problem of poor visibility during nighttime harvesting operations. This work, published in 2016, demonstrates a novel approach to balancing illumination correction with edge detail preservation, enabling robots to accurately detect and locate fruit in dim conditions. While the paper has accrued 4 citations to date, its significance lies in its targeted application: bridging the gap between theoretical image processing and practical deployment in precision agriculture. Xingqin’s research exemplifies how computer vision techniques can be tailored to solve domain-specific challenges, contributing to the broader goal of making agricultural robots more robust and reliable in uncontrolled environments. Their work remains a reference point for researchers exploring adaptive enhancement methods for autonomous systems operating under variable lighting conditions.
Research Focus
Key Achievements
Top Papers
- 1