Multi-Modal Structured Pruning for USV-Based Waterway Detection Based on Radar-Vision Fusion
Bibliographic record
Abstract
Radar-vision fusion, with more reliable performance at lower cost, has been widely used in autonomous vehicles. In the waterways, the perception of unmanned surface vessels is essential for autonomous navigation. However, the large amount of computation increases the demand for high-performance computing devices and causes severe power consumption. To reduce the computational cost and ensure reliable perception performance, we need lightweight solutions for such models. In this paper, we design a novel structured pruning framework for multi-modal perception networks. Moreover, we proposed a novel structured pruning algorithm Heterogeneous Aware SynFlow (HA-SynFlow), which prunes each modality based on its SynFlow [1] score. We prune the water surface radar-vision fusion model Achelous [2], and results show 24.0% and 7.2% improvement in Frame Per Second (FPS) on GTX1650 and Jetson Orin, respectively, with a 4.4% loss in detection mAP metric. Lastly, our pure radar pruning test shows that radar helps in long-range, occlusion, and difficult scenarios in 2D object detection task.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".