Teh Hong Khai
Papers
1
Total Citations
60
H-Index
1
About
Teh Hong Khai is a researcher at the forefront of applying deep learning to aquaculture and environmental monitoring. His work centers on computer vision, particularly the development of advanced object detection and segmentation models for underwater analysis. His most-cited paper, "Underwater Fish Detection and Counting Using Mask Regional Convolutional Neural Network" (2022, 60 citations), addresses a critical bottleneck in fish farming: the labor-intensive and error-prone process of counting hatchlings. By adapting Mask R-CNN for complex underwater environments, he demonstrated a robust, automated solution that outperforms traditional non-machine learning and earlier machine learning approaches. This contribution not only improves operational efficiency for aquaculture but also provides a scalable framework for marine biodiversity monitoring. Beyond this work, Teh Hong Khai’s research continues to push the boundaries of vision-based automation in challenging aquatic settings, making him a notable figure in the intersection of artificial intelligence and sustainable fisheries management.
Research Focus
Key Achievements
Top Papers
- 1