GhostGD-YOLOv8: An efficient algorithm for forest fire detection by UAV images
Why this work is in the frame
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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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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