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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".