Bibliographic record
Abstract
Robot-diver communication underwater is complicated by the attenuation of RF signals, the complexities of the environment in terms of deploying interaction devices, and issues related to the cognitive loading of human operators. Humans operating underwater have developed a simple yet effective strategy for diver-diver communication based on the visual recognition of gestures. Can a similar approach be effective for diver-robot communication? Here we present experiments with SCUBANet, an underwater detection dataset of body parts associated with diver-robot communication. Given the nature of standard diver gestures, here we concentrate on diver recognition and in particular on diver body-head-hand localization and examine the feasibility of using a CNN-based approach to address this problem. Such data-driven approaches typically require an appropriately annotated dataset. The SCUBANet dataset contains images of object classes commonly encountered during human-robot communication underwater. Object classes are labeled using per-instance bounding boxes. Annotations were created through crowd sourcing via a web-based interface to ease deployment. We provide baseline performance on diver and diver component recognition and localization using transfer learning on three widely available pre-trained models.
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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) | 0.000 | 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 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".