An Improved Forest Fire and Smoke Detection Model Based on YOLOv5
Bibliographic record
Abstract
Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems and humans. Deep learning techniques can adaptively learn and extract features of forest fires and smoke. However, the complex backgrounds and different forest fire and smoke features in captured forest fire images make detection difficult. Facing the complex background of forest fire smoke, it is difficult for traditional machine learning methods to design a general feature extraction module for feature extraction. Deep learning methods are effective in many fields, so this paper improves on the You Only Look Once v5 (YOLOv5s) model, and the improved model has better detection performance for forest fires and smoke. First, a coordinate attention (CA) model is integrated into the YOLOv5 model to highlight fire smoke targets and improve the identifiability of different smoke features. Second, we replaced YOLOv5s original spatial pyramidal ensemble fast (SPPF) module with a receptive field block (RFB) module to enable better focus on the global information of different fires. Third, the path aggregation network (PANet) of the neck structure in the YOLOv5s model is improved to a bi-directional feature pyramid network (Bi-FPN). Compared with the YOLOv5 model, our improved forest fire and smoke detection model at mAP@0.5 improves by 5.1%.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 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".