Yuquan Zhou
Papers
2
Total Citations
4
H-Index
1
About
Yuquan Zhou is a researcher at the forefront of intelligent industrial monitoring and autonomous robotic systems. His primary research areas encompass computer vision for anomaly detection and UAV-based autonomous exploration for environmental mapping. Zhou’s major contributions include pioneering a water leakage detection method for turbine floor equipment using Mask R-CNN, specifically designed to overcome challenges posed by weak and uneven lighting in industrial settings—a critical advancement for reducing economic losses and safety hazards. His work has garnered early recognition, with this foundational paper accumulating 3 citations. More recently, Zhou has advanced the field of autonomous robotics with his work on "HFCH: Hybrid Frontier Guided Fast UAV Autonomous Exploration," which enables complete and high-quality mapping in unknown environments. This innovative approach, published in 2025, demonstrates his commitment to pushing the boundaries of real-time, efficient exploration. Zhou’s research not only addresses pressing industrial safety needs but also lays the groundwork for next-generation autonomous systems, marking him as an emerging leader in applied AI and robotics.
Research Focus
Key Achievements
Top Papers
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- 2