Robotic Coral Reef Health Assessment Using Automated Image Analysis
Bibliographic record
Abstract
This paper presents a system capable of autonomous surveillance and analysis of coral reef ecosystems using natural lighting. We describe our strategy to safely and effectively deploy a small marine robot to inspect a reef using its digital cameras. Image analysis using a (RBF‐SVM) radial basis function‐support vector machines in combination with (LBP) local binary pattern, Gabor and Hue descriptors developed in this work are able to analyze the resulting image data automatically and reliably by learning from the annotations of expert marine biologists. Our primary evaluation is performed on a novel coral data set that we collected during a series of robotic ocean deployments, the MRL Coral Identification Challenge. We have also applied our algorithms to a data set of coral imagery previously published by other researchers. Our algorithms recognize coral images in our own challenging data with 88.9% accuracy, while being sufficiently efficient to run online on our vehicle. This demonstrates the feasibility of such a system for practical use for the preservation of this crucial ecological resource.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".