Papers
6
Total Citations
134
H-Index
4
About
Huaming Qian is a researcher whose work bridges reliability engineering, robotics, and cutting-edge computer vision. His primary research areas include time-variant reliability analysis, lightweight object detection, and sensor fusion for mobile robotics. Qian’s most impactful contribution is his 2020 paper on time-variant reliability analysis for industrial robot RV reducers using Kriging models, which has garnered 78 citations and provides a critical framework for predicting failures in robotic joints under multiple failure modes. In recent years, he has focused on making deep learning models more efficient for real-world deployment, developing lightweight object detection methods like L-SSD (23 citations) and an attention-enhanced YOLOv4 for security scenes (19 citations). His work on dynamic point-line SLAM (2025) integrates lightweight detection with simultaneous localization and mapping, pushing the boundaries of autonomous navigation. Notably, his earlier 2009 paper on fuzzy heuristic reduction of gyro drift in MEMS-based mobile robot tracking remains a foundational reference for addressing sensor drift, a persistent challenge in robotics. Qian’s research consistently demonstrates a practical, application-driven approach, from industrial reliability to efficient AI, making his work valuable for engineers and researchers developing robust, real-time robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2L-SSD: lightweight SSD target detection based on depth-separable convolution23 citations · 2024
- 3
- 4Dynamic point-line SLAM based on lightweight object detection6 citations · 2025
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