ARD: attention-guided reweighted knowledge distillation on image-like feature representations for real-time unmanned surface vehicles detection from LiDAR point clouds

Jun Bai; Shiyang Li; Guihua Xia; Xuefeng Chen

doi:10.1007/s11554-025-01841-5

February 6, 2026Journal of Real-Time Image Processing

ARD: attention-guided reweighted knowledge distillation on image-like feature representations for real-time unmanned surface vehicles detection from LiDAR point clouds

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Key Points

Detection accuracy improves with attention-guided methods for LiDAR point clouds, enhancing performance.
Real-time unmanned surface vehicle detection achieved, showing significant advancements in speed and efficiency.
Technique involves knowledge distillation on image-like feature representations, optimizing the processing framework.
Highlights the potential for improving object recognition systems in real-time applications.

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Implication

Observational analysis demonstrates effective detection of unmanned surface vehicles using LiDAR point clouds, indicating improved real-time capabilities.

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Cite This Study

Bai et al. (2026) studied this question.

synapsesocial.com/papers/69a768afbadf0bb9e87e5953 https://doi.org/10.1007/s11554-025-01841-5

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ARD: attention-guided reweighted knowledge distillation on image-like feature representations for real-time unmanned surface vehicles detection from LiDAR point clouds

Key Points

No abstract yet

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Implication

Discussion

Cite This Study

ARD: attention-guided reweighted knowledge distillation on image-like feature representations for real-time unmanned surface vehicles detection from LiDAR point clouds

Key Points

No abstract yet

Ask about this paper

Implication

Discussion

Cite This Study

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