Evidential Reasoning for Ship Classification: Fusion of Deep Learning Classifiers
Bibliographic record
Abstract
Maritime Situational Awareness (MSA) is becoming critical in a world dominated by complex maritime civilian or military activities, diplomatic and ecological challenges for all coastal countries. Ship tracking, detection and classification through automated surveillance systems help improve the MSA. This paper proposes a new approach to ship classification from an airborne surveillance platform, such as the Aurora CP140. The proposed approach is based on the combination of several deep learning classifiers, using evidential reasoning (Dempster-Shafer theory) to better take into account the uncertainty at the last layer of the classifier. The traditional softmax layer is replaced with more adequate layers to model the uncertainty. Such layers are based on the min-max or ReLu scalings, jointly with additional modeling of the uncertainty. Results obtained from maritime observation videos are compared: the evidential fusion approach provides better classification results than the initial Bayesian classifier.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".