Huibing Wang
Papers
8
Total Citations
125
H-Index
5
About
Huibing Wang is a computer vision researcher specializing in underwater object detection, marine robotics, and intelligent perception systems. His work addresses some of the most challenging problems in ocean exploration, where degraded imaging conditions, biological camouflage, and environmental unpredictability make automated detection exceptionally difficult. Wang's most impactful contribution lies in advancing attention-based detection frameworks, particularly through innovative extensions of the YOLO architecture. His 2022 paper on refined marine object detection, incorporating spatial pyramid pooling and bidirectional feature fusion, has garnered 43 citations, establishing him as a notable voice in marine computer vision. He has since developed a rich series of attention mechanisms — spanning multiple dimensions, functions, and levels — alongside unsupervised clustering optimization approaches, collectively accumulating over 60 citations across these works. Beyond detection, Wang has made meaningful contributions to underwater image enhancement, addressing the overlooked problem of artificial light distortion in AUV-captured imagery, and to depth map stitching for spatial understanding. His earlier work on intelligent seafood capture systems reflects a consistent commitment to translating research into practical robotics applications. Together, his portfolio represents a coherent research vision: enabling autonomous underwater robots to perceive, interpret, and interact with the ocean environment reliably and intelligently.
Research Focus
Key Achievements
Top Papers
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- 7A depth map stitching framework based on salient region matching3 citations · 2024
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