Automated species classification and counting by deep-sea mobile crawler platforms using YOLO
Bibliographic record
Abstract
Edge computing on mobile marine platform is paramount for automated ecological monitoring. The goal of demonstrating the computational feasibility of an Artificial Intelligence (AI)-powered camera for fully automated real-time species-classification on deep-sea crawler platforms was searched by running You-Only-Look-Once (YOLO) model on an edge computing device (NVIDIA Jetson Nano), to evaluate the achievable animal detection performances, execution time and power consumption, using all the available cores. We processed a total of 337 rotating video scans (∼180°), taken during approximately 4 months in 2022 at the methane hydrates site of Barkley Canyon (Vancouver Island; BC; Canada), focusing on three abundant species (i.e., Sablefish Anoplopoma fimbria, Hagfish Eptatretus stoutii, and Rockfish Sebastes spp.). The model was trained on 1926 manually annotated video frames and showed high detection test performances in terms of accuracy (0.98), precision (0.98), and recall (0.99). The trained model was then applied on 337 videos. In 288 videos we detected a total of 133 Sablefish, 31 Hagfish, and 321 Rockfish nearly in real-time (about 0.31 s/image) with very low power consumption (0.34 J/image). Our results have broad implications on intelligent ecological monitoring. Indeed, YOLO model can meet operational-autonomy criteria for fast image processing with limited computational and energy loads.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".