Beyond sRGB: Optimizing Object Detection with Diverse Color Spaces for Precise Wildfire Risk Assessment
Bibliographic record
Abstract
Forest fire risk assessment and prevention are crucial topics in environmental management. The most popular method involves using drone imagery and object detection models to analyze risk. However, traditional drone images typically use the sRGB color space, which may lose valuable information. In this study, we systematically investigate the impact of different color spaces (sRGB, Linear RGB, Log RGB, XYZ, LMS, and D-Log) on the performance of state-of-the-art vision transformer models and the latest YOLO model for tree condition detection. Our experiments demonstrate that Log RGB and Linear RGB significantly outperform the conventional sRGB color space, with Log RGB achieving a 27.16% improvement in mean average precision (mAP) and a 34.44% gain in mean average recall (mAR). These improvements are attributed to Log RGB’s enhanced dynamic range, superior illumination invariance, and better information preservation, which enable the detection of subtle environmental details crucial for early wildfire risk assessment. Overall, our findings highlight the potential of leveraging alternative color space representations to develop more accurate and robust tools for wildfire risk assessment.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".