Depth-Homography Registration Framework and YOLOv8n-Coordinate Attention Forest Fire Detection for Visible-Infrared UAV Imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A novel depth-homography model for Infrared (IR) and Visible (RGB) images registration and YOLOv8n-CoordAttn detection model for wildfire detection are presented. In low-light and smoke-occluded conditions, fire detection using IR images performs better than RGB images, while RGB images may still complement IR information as heat radiation around the fire makes the fire boundary blurry in the thermal imagery. Hence, fire detection based on image fusion between IR and RGB images is a more reliable approach. For image alignment between these two modalities, camera calibration is widely used, while in this work, an innovative depth-homography model as a simpler and yet precise alternative is presented, which estimates the homography matrix for an arbitrary depth with which the image alignment is conducted. Moreover, YOLOv8n-CoordAttn is presented, where YOLOv8n is augmented with Coordinate Attention modules. This detection model predicts bounding boxes of fire spots based on multispectral IR-RGB images, aiming to improve accuracy while still conducting inference in real-time. Also, outdoor flight tests using a DJI M300 UAV equipped with an H20T camera system in daytime and nighttime are carried out to gather IR-RGB datasets for training and evaluating the depth-homography and YOLOv8n-CoordAttn detection models, whose video demonstration is available at https://www.youtube.com/watch?v=Hq6X-FcUVss.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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