Wild salmon enumeration and monitoring using deep learning empowered detection and tracking
Bibliographic record
Abstract
Pacific salmon have experienced declining abundance and unpredictable returns, yet remain vital to livelihoods, food security, and cultures of coastal communities around the Pacific Rim, creating a need for reliable and timely monitoring to inform sustainable fishery management. Currently, spawning salmon abundance is often monitored with in-river video or sonar cameras. However, reviewing video for estimates of salmon abundance from these programs requires thousands of hours of staff time, and data are typically not available until after the fishing season is completed. Computer vision deep learning can enable rapid and reliable processing of data, with potentially transformative applications in salmon population assessment and fishery management. Working with two First Nations fishery programs in British Columbia, Canada, we developed, trained, and tested deep learning models to perform object detection and multi-object tracking for automated video enumeration of salmon passing two First Nation-run weirs. We gathered and annotated more than 500,000 frames of video data encompassing 12 species, including seven species of anadromous salmonids, and trained models for multi-object tracking and species detection. Our top performing model achieved a mean average precision (mAP) of 67.6%, and species-specific mAP scores > 90% for coho and > 80% for sockeye salmon when trained with a combined dataset of Kitwanga and Bear Rivers’ salmon annotations. We also tested and deployed a prototype for a real-time monitoring system that can perform computer vision deep learning analyses on site. Computer vision models and off-grid monitoring systems show promise for automated counting and species identification. A key future priority will be working with stewardship practitioners and fishery managers to apply salmon computer vision, testing and applying edge-capable computing solutions for in-situ analysis at remote sites, and developing tools for independent user-led computer vision analysis by non-computer scientists. These efforts can advance in-season monitoring and decision making to support adaptive management of sustainable wild salmon fisheries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".