Depth-Homography Registration Framework and YOLOv8n-Coordinate Attention Forest Fire Detection for Visible-Infrared UAV Imagery
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".