Computer Vision Based Intelligence for Tracking and Predicting Whale Blow Trajectories
Bibliographic record
Abstract
Collecting blow samples from whales allows for studies on individual and population health. However, it is important to not interfere or harm the whales, forcing collections to happen from a distance. An Uncrewed Aerial Vehicle (UAV) equipped with a collection system is capable of collecting whale blow samples without interfering with the whale by flying through the blow cloud. The work presented describes a computer vision based intelligence system for facilitating whale blow collection using an UAV. The computer vision system utilizing Ultralytics Neural Network You Only Look Once (YOLO) version 8. Using the timing of YOLO's outputs, an averaging technique is used to predict future blow timings from past ones. Additionally, an Auto Regressive Moving Average (ARMA) model is used in combination with the bounding boxes provided by YOLO in order to predict where the blow centres will be in the near future. It is shown that YOLO is able to successfully identify a whale, its head, and the blow while the ARMA model is able to predict where the blow will be sufficient accuracy for collection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".