A Comprehensive Analysis of Transfer Learning Algorithms for Image Segmentation of Irregular-Shaped Fire Object
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Bibliographic record
Abstract
This study investigates the application of transfer learning and attention mechanisms to improve image segmentation for fire detection, particularly for irregularly shaped fire regions.Five models, including U-Net and its variants with VGG16, ResNet50, DenseNet201, and EfficientNet-B7 backbones, were developed and evaluated with and without attention layers.A dataset of 5,000 images, segmented into training, validation, and test sets, was prepared, focusing on flame regions.Experimental results demonstrated that attention-based models consistently outperformed their non-attention counterparts, with the VGG16 U-Net attention model achieving the highest validation IoU score of 0.8220.By effectively capturing intricate fire boundaries, these models offer significant improvements in segmentation accuracy.The findings highlight the potential of combining attention mechanisms and transfer learning for real-time fire detection systems.
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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 | 0.002 |
| 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 | 0.000 |
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 it