| --- |
| license: agpl-3.0 |
| pipeline_tag: object-detection |
| tags: |
| - yolo |
| - yolo11 |
| - ultralytics |
| - wildfire |
| - smoke-detection |
| - early-warning |
| --- |
| |
| # FireSmockDetect — wildfire smoke detectors |
|
|
| Trained checkpoints from the FireSmockDetect project |
| (code, protocols and full reports: https://github.com/TianchenGuan/FireSmockDetect). |
| Task: single-class ("smoke") detection on RGB images from fixed lookout |
| cameras and drones, tuned for FAINT early-stage plumes. |
|
|
| ## Checkpoints |
|
|
| | file | arch | params | train data | role | |
| |---|---|---|---|---| |
| | `expB_y11s_deployed.pt` | YOLO11s @1280 | 9.4 M | pyro-sdis train + D-Fire train | production default of the project service | |
| | `v5bA_y11m_best.pt` | YOLO11m @1280 | 20.1 M | same recipe, retrained (v5b arm A, seed 3) | best fixed-camera model on the frozen test | |
| | `v6A_y11n/v6A_y11n_best.pt` | YOLO11n @1280 | 2.59 M | pyro-sdis train + D-Fire train (real only) | v6 edge model (+ ONNX / TFLite fp16 / TFLite INT8 3.6 MB) | |
|
|
| ## Headline numbers (frozen pyro-sdis TEST, matched negative-image FP rate, IoU≥0.4) |
|
|
| | model | R@5%FP | R@10%FP | R@20%FP | |
| |---|---|---|---| |
| | v5bA_y11m_best | 0.287 | **0.388** | 0.553 | |
| | expB_y11s_deployed | 0.258 | 0.354 | 0.501 | |
|
|
| Full per-source tables, aerial-vs-fixed splits, and negative results (data, |
| distillation and ensemble variants that did NOT beat these) are in the GitHub |
| reports (`reports/v5b/FINAL_REPORT_V5B.md`). |
|
|
| ## Usage |
|
|
| ```python |
| from ultralytics import YOLO |
| model = YOLO("v5bA_y11m_best.pt") |
| res = model.predict("image.jpg", imgsz=1280, conf=0.10) |
| ``` |
|
|
| Pick the operating confidence from your own tolerated false-positive rate; |
| the project calibrates thresholds per deployment scenario (fixed camera vs |
| aerial) — see the repo's eval tooling. |
|
|
| ## Limitations |
|
|
| - Faint-smoke recall at low FP is the binding constraint (see table); this is |
| an assistance tool, not a certified alarm system. |
| - Thresholds do NOT transfer between fixed-camera and aerial imagery. |
| - No early-warning (time-to-first-alert) claim is made: qualified smoke-onset |
| footage was unavailable (documented in the v5 protocol). |
| - Training data: pyro-sdis and D-Fire (original authors' labels). FIgLib / |
| HPWREN (CC BY-NC-ND) was never used for training. |
|
|
| ## License |
|
|
| AGPL-3.0 (derivatives of Ultralytics YOLO11). Dataset licences remain with |
| their owners. |
|
|
|
|
| ## v6 edge release (YOLO11n, real-data-only) |
|
|
| `v6A_y11n/` contains the frozen v6 deliverable: PyTorch checkpoint, ONNX, |
| TFLite fp16 and TFLite INT8 (3.6 MB), plus `infer.py` — a single-command |
| offline image/video inference script (PyTorch / ONNX / TFLite backends) — |
| and the export report. |
|
|
| Frozen pyro-sdis TEST (matched negative-image FP, IoU≥0.4): R@5% 0.243, |
| R@10% 0.344 — the best nano-scale model of the v6 matrix. Deployment |
| validation of the INT8 file itself on the project val set: no loss at the |
| 2%/5% FP operating points, −2.1 pt at 10% (traced to graph conversion, not |
| quantization; fp16 TFLite is bit-identical in accuracy). Suggested |
| confidence thresholds from the INT8 file's own val curve: 0.73 / 0.68 / |
| 0.62 for ~2 / 5 / 10 % negative-image FP. CPU x86 median forward latency |
| 45.8 ms (proxy; on-device latency unmeasured). |
|
|
| v6 synthetic-data result (pre-registered, reported as-is): neither |
| procedural compositing nor repaired generative image-to-image synthesis |
| improved real-data evaluation at matched optimizer steps; seed spread |
| (±3 pt) exceeded all between-arm differences. Full protocol and reports in |
| the GitHub repo (`reports/v6/`, `reports/v6/repair/`, `reports/v6/sprint/`). |
|
|
