Huijuan Lu

Guilin University of Electronic Technology, China University of Mining and Technology

Papers

Total Citations

H-Index

About

Huijuan Lu is a researcher whose work bridges the frontiers of machine vision, deep learning, and intelligent robotics, with a particular focus on solving real-world industrial and safety challenges. Her most-cited study, “Research on Multi-Hole Localization Tracking Based on a Combination of Machine Vision and Deep Learning” (2024, 9 citations), addresses a critical bottleneck in automated manufacturing: the inefficiency and danger of manual workpiece assembly. By fusing machine vision with deep learning, she developed a system capable of precise multi-hole localization tracking, offering a path toward safer, more efficient industrial automation. In earlier work, “Mobile Robot Odor Source Localization Based on Modified FWA” (2018, 4 citations), Lu tackled the hazardous problem of detecting chemical leak sources. She enhanced the Fireworks Algorithm (FWA) to enable mobile robots to autonomously navigate and locate odor sources in complex, risky environments—a contribution with clear implications for industrial safety and environmental monitoring. Though her citation counts are modest, Lu’s research demonstrates a clear, applied focus: leveraging AI and robotics to replace dangerous human tasks with intelligent automation. Her work is particularly relevant for students and researchers interested in the intersection of computer vision, deep learning, and autonomous systems for industrial applications.