Papers
1
Total Citations
6
H-Index
1
About
Qiushi Pan is a researcher whose work sits at the intersection of computer vision and autonomous robotics, with a particular focus on real-time scene understanding for unmanned systems. Pan’s most-cited contribution, “A scene classification algorithm of visual robot based on Tiny Yolo v2” (2019, 6 citations), addresses a critical bottleneck in autonomous navigation: the need for fast, accurate scene classification. By proposing an end-to-end multi-object framework built on the lightweight Tiny Yolo v2 architecture, Pan improved upon traditional convolutional neural network approaches, which often suffer from low accuracy in dynamic environments. This work directly supports robots and unmanned vehicles in performing context-aware actions—such as adjusting speed or path planning—based on scene recognition. Though early in citation impact, the research signals a practical shift toward efficient, deployable models for embedded vision systems. Pan’s focus on balancing computational efficiency with classification fidelity positions their work as a stepping stone for resource-constrained autonomous platforms, offering a blueprint for future optimizations in real-time robotic perception.
Research Focus
Key Achievements
Top Papers
- 1A scene classification algorithm of visual robot based on Tiny Yolo v26 citations · 2019