GhostGD-YOLOv8: An efficient algorithm for forest fire detection by UAV images
Bibliographic record
Abstract
In this study, an improved YOLOv8 algorithm is designed specifically for forest fire detection task using unmanned aerial vehicle (UAV) images. In the backbone structure of YOLOv8, the GhostConv and C3Ghost modules are innovatively integrated to replace the original Conv and C2f modules. These modules possess excellent lightweight characteristics, which significantly reduce the computational load of the model while effectively improving the efficiency and quality of feature extraction.This enables the model to perform more robustly when processing complex forest scene images. Additionally, in the neck structure design, the gather-and-distribute architecture is employed for further optimization, which optimizes the feature transfer and fusion mechanism through its unique design,thereby enhancing the interaction between features across different scales. Experimental results demonstrate that the improved YOLOv8 algorithm exhibits superior performance in forest fire detection tasks. Compared with the original YOLOv8 model, improvements can be observed in detection accuracy, recall rate, and precision.The enhanced model can effectively address complex scenarios such as smoke occlusion and lighting variations in forest environments, providing robust technical support for early and accurate monitoring and warning of forest fires. It is anticipated to play a critical role in practical forest fire prevention efforts.
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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 | 0.000 |
| 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 | 0.001 |
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".