Xiang‐Rong Qin
Papers
1
Total Citations
29
H-Index
1
About
Xiang‐Rong Qin is a leading researcher at the intersection of computer vision and underwater robotics, with a primary focus on advancing object detection for marine environments. His most notable contribution is the development of YOLO8-FASG, a high-accuracy fish identification method specifically designed to overcome the persistent challenge of detecting small, fast-moving fish in underwater robotic systems. This work, published in 2024 and already garnering 29 citations, addresses critical limitations in model flexibility and receptive field, enabling more precise identification of targets that occupy minimal screen space. Qin’s research directly enhances the capabilities of autonomous underwater vehicles, with significant implications for marine biology, environmental monitoring, and sustainable fisheries management. By tackling the unique difficulties of dynamic underwater scenes, his innovations improve both the reliability and efficiency of robotic vision systems. With a growing citation impact, Qin is establishing himself as a key contributor to applied deep learning in challenging real-world settings, bridging the gap between theoretical advances and practical robotic deployment.
Research Focus
Key Achievements
Top Papers
- 1