Chuansheng Yang
Papers
1
Total Citations
9
H-Index
1
About
Chuansheng Yang is a researcher whose work sits at the intersection of deep learning and marine robotics, with a particular focus on efficient object detection in challenging underwater environments. His most-cited paper, "Underwater Fish Object Detection based on Attention Mechanism improved Ghost-YOLOv5" (2022, 9 citations), addresses a critical bottleneck in autonomous underwater monitoring: the need for high-accuracy detection models that can run on computationally constrained robotic platforms. Yang’s key contribution lies in adapting the YOLOv5 architecture—a lightweight, real-time object detector—by integrating attention mechanisms to enhance feature extraction in murky, low-visibility underwater settings. This work is notable for balancing model efficiency with detection precision, directly enabling practical deployment on underwater robots for tasks like fish population monitoring and ecological surveillance. By tackling the trade-off between computational cost and accuracy, Yang’s research provides a scalable solution for real-time marine object detection, bridging the gap between state-of-the-art deep learning and real-world aquatic applications. His work is particularly valuable for students and engineers developing autonomous systems for environmental monitoring, offering a concrete example of how to optimize neural networks for specialized, resource-limited scenarios.
Research Focus
Key Achievements
Top Papers
- 1