A forest fire detection method by a modified YOLOv8 algorithm with feature alignment and fusion strategy
Bibliographic record
Abstract
Forest fires pose a serious threat to the ecological environment and human safety. Timely and accurate early fire identification is crucial for disaster prevention and control. In this paper, a high-precision forest fire detection method is proposed using dual-images captured by unmanned aerial vehicle (UAV). Based on the improved YOLOv8 architecture, the method effectively improves the accuracy of fire detection by fusing RGB and thermal images. In order to solve the problem of dual-modal feature space dislocation, a cross-modal feature alignment module is designed to realize the unity of feature domains. Based on the physical characteristics of dual-modal images, a dynamic feature fusion module is designed to realize adaptive feature fusion. GhostConv lightweight convolution and FastC2f structure are introduced to compress the number of model parameters to 3.3MB. Experiments on the FLAME2 public dataset show that the proposed method achieves an AP value of 98.3%, which is significantly better than the existing methods in terms of detection accuracy and model parameters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 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".