3-D Dynamic Multitarget Detection Algorithm Based on Cross-View Feature Fusion
Bibliographic record
Abstract
In autonomous driving, data degradation and insufficient feature-richness in the current single-modal algorithms can not effectively perform dynamic multi-target detection. Therefore, a 3D dynamic multi-target detection algorithm based on cross-view feature fusion is proposed. A two-stage parallel fusion framework is proposed, which simultaneously extracts point cloud and image features in the first stage. Additionally, a Lidar-Camera feature mapping module is designed to achieve point-wised correspondence between different data. Then, a feature weighted fusion module is designed to judge the weight of each point in the point cloud feature and image feature. In the second stage, a keypoint-based feature extraction module is designed to enrich the features, which integrates the multi-scale features and image features in the first stage to improve the detection accuracy. The proposed algorithm was compared with other SOTA methods on the Kitti, Waymo and Nuscene datasets. The result showed that the accuracy of vehicle target has reached to 93.03%. The module ablation study and accuracy detection on self-made dataset showed that the proposed algorithm not only had good robustness, strong portability and generalization ability, but also had high detection accuracy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".