Papers
1
Total Citations
55
H-Index
1
About
Xiaoke Hou is a researcher at the forefront of applying deep learning and embedded systems to marine science. His work centers on real-time marine animal image classification, leveraging lightweight neural networks like MobileNet and transfer learning to overcome the computational constraints of underwater environments. His most cited paper, "Real-time Marine Animal Images Classification by Embedded System Based on Mobilenet and Transfer Learning" (2019, 55 citations), demonstrates a practical, high-efficiency solution for monitoring marine growth, fishing activities, and water conditions—critical for sustainable aquaculture. By enabling accurate, low-power classification directly on embedded devices, Hou’s contributions bridge the gap between advanced AI and field-deployable marine technology. His research not only advances automated underwater monitoring but also offers a scalable framework for real-time ecological surveillance, making him a key figure in the intersection of computer vision, edge computing, and marine biology.
Research Focus
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Top Papers
- 1