Minh DoNgoc
Papers
1
Total Citations
3
H-Index
1
About
Minh DoNgoc is a rising researcher in computer vision and autonomous underwater systems, focusing on the critical challenge of depth estimation for autonomous underwater vehicles (AUVs). His work addresses the fundamental problem that deep neural networks, while powerful, struggle in the degraded visual conditions of underwater environments—characterized by low light, scattering, and color distortion. In his highly cited 2024 paper, "Underwater Image Enhancement for Depth Estimation via Various Image Processing Techniques," DoNgoc systematically evaluates how pre-processing methods can improve depth perception accuracy, bridging the gap between classical image enhancement and modern deep learning approaches. Though early in his career, his contributions are already gaining traction, with his work cited by researchers tackling similar challenges in marine robotics and underwater scene understanding. DoNgoc’s research is particularly timely as AUVs become more prevalent in ocean exploration, pipeline inspection, and environmental monitoring. By demonstrating that strategic image enhancement can significantly boost depth estimation performance without requiring massive new datasets, he offers a practical, computationally efficient pathway for improving real-world underwater navigation systems.
Research Focus
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Top Papers
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