Use of convolutional neural networks to automate the detection of wildlife from remote cameras
Bibliographic record
Abstract
Determining the abundance, distribution, and habitat associations of wildlife species is important for understanding their ecology, behavior and conservation. For mobile, rare, and wide-ranging species, biologists often obtain this information from remote cameras and time-lapse photography. The captured images are then visually inspected to identify those that contain useful information. Due to the large number of images to be processed, the task of visual inspection is painstaking and tedious. In this paper, we describe preliminary results of an automated screening system that is intended to alleviate this problem. Specifically, we study the problem of detecting grizzly bears (Ursus acrtos) in still images, using a convolutional neural network (CNN). Given each image, we first use the Maximally Stable Extremal Regions (MSER) to segment sub-regions that potentially contain a bear and then apply a pre-trained convolutional neural network as the classifier to determine if a bear is present in a sub-region. Experimental results from a real-world dataset demonstrate that our system is able to eliminate over 90% of the images from human inspection while recalling over 60% of the positive images that contain a bear, at a rate of approximately one minute per image.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".