The Influence of Input Image Scale on Deep Learning-Based Beluga Whale Detection from Aerial Remote Sensing Imagery
Bibliographic record
Abstract
This paper investigates the influence of input image scale on deep learning-based Beluga whale detection from aerial remote sensing imagery. Beluga whales in the Arctic are jeopardized due to increased coastal activities and climate change. Aerial survey is a common population counting method, and it can be laborious and exhausting to count the number of whales manually. Convolutional neural networks (CNNs) have greatly improved the performance of detecting and counting whales. Since most remote sensing images are very high in resolution, it is a common practice to slice the image into small patches. In this work, we input the full image (after resizing) into an object detection model and compare its performance with the sliding window approach. Experimental results suggest that increasing the input image size helps improve the model’s performance, and the model is able to learn the contextual information.
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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".