Detecting Unexpected Marine Species with Underwater Cameras and Deep Learning
Bibliographic record
Abstract
Integrating machine learning with audio and video monitoring for automating marine ecosystem monitoring is in its early stages but advancing rapidly. Typically, these models exhibit robust performance on marine classes and environments on which they have been trained; however, they often misclassify previously unseen marine classes with high confidence, erroneously assigning them to known categories. This study addresses the challenge of detecting previously unseen marine categories in underwater video data, while accurately classifying the examples of seen classes. We propose a system that leverages the features extracted from machine learning classifiers, and unseen marine classes from the publicly available OzFish dataset to accurately identify unobserved species in a low-lit, low-resolution underwater video dataset obtained by the Department of Fisheries and Oceans, Canada. We report accuracy scores of 83.0% and 85.2% for ViT and ResNet18 architectures, respectively, on an extensive test set of 46,641 images with both seen and unseen classes. This methodology marks a significant advancement toward the development of automated, scalable marine monitoring systems capable of adapting to varying species distributions and environmental changes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".