Papers
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Total Citations
3
H-Index
1
About
Hua Ye is a researcher specializing in computer vision and deep learning, with a particular focus on underwater object detection—a challenging domain where low contrast, blurred imagery, and small, clustered targets often confound traditional methods. Ye’s major contribution lies in developing enhanced lightweight models that balance accuracy with computational efficiency, addressing real-world constraints in marine robotics and environmental monitoring. Their most cited work, "BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model" (2025, 3 citations), introduces a novel architecture that mitigates omission and false positives in complex underwater scenes, achieving robust multi-scale detection despite severe visual degradation. This innovation not only advances the state-of-the-art in autonomous underwater systems but also demonstrates practical applicability for tasks like species identification and debris mapping. Ye’s research is notable for its focus on deployable solutions, bridging the gap between high-performance detection and resource-limited platforms. With a growing citation footprint, Ye is establishing a reputation for tackling niche yet critical problems in visual perception, making their work essential reading for students and engineers interested in edge AI, marine science, and robust computer vision under adverse conditions.
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Top Papers
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