Pre-processing for UAV Based Wildfire Detection: A Loss U-net Enhanced GAN for Image Restoration
Bibliographic record
Abstract
In this paper, a U-net with feature loss enhanced generative adversarial network (GAN) is designed for the wildfire or smoke images restoration which is captured by unmanned aerial vehicles in a serious environment. Based on the concepts of GAN, feature loss, and fastai API, we firstly crappy the target images, and train a U-net architecture based generator, then load the adaptive loss of discriminator and the mean square error together to train the GAN model. After the GAN, a second U-net grabs the feature loss from an Imagenet pre-trained loss network to generate the GAN output images with one more step. This U-net enhanced the generator of GAN and helped to get the main features in human conception. Comparing with other restoration methods, this model used the adaptive loss to train the GAN and perceptual loss to train the next U-net. Learning rate with simulation annealing helped jumping out of the local minimum. The result proved the good performance of this model.
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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 | 0.001 |
| 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".