Chenglong Hou
Papers
1
Total Citations
18
H-Index
1
About
Chenglong Hou’s research focuses on the intersection of computer vision and marine aquaculture, with a particular emphasis on underwater target detection. His most-cited work, “An Improved YOLOv5s-Based Scheme for Target Detection in a Complex Underwater Environment” (2023), addresses a critical challenge in the seafood industry: the dangers faced by divers during manual fishing operations. By enhancing the YOLOv5s deep learning model, Hou developed a more accurate and efficient system for detecting sea cucumbers, sea urchins, and other marine organisms in murky, low-visibility underwater conditions. This innovation not only improves the safety of fishing practices but also advances automated aquaculture monitoring. With 18 citations since its publication, Hou’s work is gaining traction among researchers in marine robotics and precision aquaculture. His contributions are particularly notable for their practical impact, offering a scalable solution to reduce human risk while increasing operational efficiency. Hou’s research exemplifies how deep learning can be tailored to real-world environmental constraints, making him a rising voice in the field of intelligent marine systems.
Research Focus
Key Achievements
Top Papers
- 1