Smoke Detection Model Based on Adaptive Feature Extraction Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Efficient detection of smoke plays a critical role in preventing and suppressing fires. However, smoke is generally of variable shapes and colors, blurred borders, and irregular textures, which makes smoke detection based on deep learning a challenging task. Aiming at this problem, a smoke detection adaptive deep model named DB-YOLO is proposed. In the model, Spatial attention-based Dynamic Convolution kernel (SDConv) is designed and embedded as a feature extraction block to improve the ability of extracting representative features from images of diverse textures. Besides, an improved Bi-directional Feature Pyramid Network (BiFPN) is integrated as a feature fusion block to fuse multi-scale features. Results show that mAP@0.5 of the DB-YOLO can increase by 6.08% to 14.40% in smoke dataset compared to currently popular object detection models.
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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 | 0.000 |
| 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 | 0.000 |
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