🔥 FireGuard

Fire & smoke detection for indoor CCTV — one notebook from raw datasets to a deployable model.

Status — v0.2. The training pipeline is complete and tested; accuracy numbers are not filled in yet (see Results). v0.1 shipped an inference demo on third-party checkpoints of unknown provenance — v0.2 replaces those with a model trained on documented, licensed data.

🇮🇷 خلاصهٔ فارسی در انتهای فایل.

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Scope

FireGuard targets indoor and urban CCTV — homes, offices, shops. Not wildfire towers, not drones.

That choice drives everything else: which datasets are useful, which augmentations matter, and why the model is evaluated on false-alarm rate rather than mAP alone.

Classes	0 = fire, 1 = smoke — two, nothing else
Input	wide-angle IP cameras, 320×240 → 720p, heavy compression, IR night mode
Detector	YOLO26 (n/s/m), fine-tuned from COCO weights
Metric that matters	false alarms per camera per 24 h, then time-to-detection

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Quickstart

▶ Open FireGuard_Pipeline.ipynb in Colab → Runtime → Change runtime type → T4 GPU → run the cells in order.

One notebook, 14 sections, dataset download through TensorRT export:

§	Step	Every session?
1–2	Install, hardware auto-tune, settings	✅
3	Parallel dataset download → Drive	first run
4–5	Merge, class fix, dedup, group-aware split, audit	first run
6–8	CCTV augmentation, 🚁 3-minute smoke test, baseline	first run
9	🔬 A/B ablation on augmentation	optional
10	Training — progressive resolution, auto-resume	✅
11–14	Evaluation, operating point, export, model card	after training

Weights land in MyDrive/FireGuard_Runs/<RUN>/final/ with a model_card.json.

Do not hand-edit the notebook — edit build_pipeline_notebook.py and regenerate:

python build_pipeline_notebook.py

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Data

Dataset	Images	Composition	Format	License
FASDD_CV	95,126	39,114 negative · 23,350 smoke · 20,126 both · 12,536 fire	YOLO / VOC / COCO	CC BY 4.0
D-Fire	21,527	9,838 negative · 5,867 smoke · 4,658 both · 1,164 fire	YOLO	free
Merged	~113k	~37k with fire · ~53k with smoke · ~48k negative	YOLO	—

FASDD_CV is the backbone: it explicitly spans indoor/outdoor, day/night, near/far, and surveillance cameras. D-Fire contributes its 9,838 deliberately-confusing negatives — sunsets, lamp glare, cloud-that-looks-like-smoke.

Deliberately excluded: Pyro-SDIS, FIgLib, FLAME (wildfire towers and drones — wrong domain) and DetectiumFire (CC BY-NC, unusable in a product).

Numbers above were measured directly from the archives, not quoted from papers — see DATASET_AUDIT.md.

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Traps this pipeline handles

Each of these was measured, not assumed. Any of them silently ruins a naive merge.

#	Trap	Handling
1	Class maps are opposite. FASDD is 0=fire, D-Fire is 0=smoke	D-Fire labels flipped with 1-c
2	D-Fire's AoF split is consecutive video frames — adjacent frames measured 80–90 % similar	group-aware split
3	Those same-event frames sit 6–13 bits apart — far outside the dedup threshold of 3	second threshold: distance 4–12 → keep both, lock to one split
4	~39k plain negatives (sky, walls) share identical hashes and formed a bucket the pairwise search skipped	exact-hash pass in O(n) before near-duplicate search
5	imgsz > 640 is wasted — FASDD images are capped at 640 px on the long side	hard ceiling at 640
6	resume=True overwrites every arg from the checkpoint (trainer.py: self.args = get_cfg(ckpt_args))	each resolution stage is a fresh train(); resume only within a stage
7	Google Drive is very slow with many small files	data and weights on local disk; Drive sync on a background thread
8	Public datasets are clean web images; real CCTV is not	compression / downscale / grayscale / motion-blur augmentation via Ultralytics' official augmentations= hook

A known label-semantics caveat: FASDD labels candles and matches as fire. Left in for now — the plan is to separate them in the decision layer by size and persistence rather than by deleting data.

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Results

Not yet measured. Filled in after the first full training run.

Metric	Value
mAP@50 — overall	—
mAP@50 — fire	—
mAP@50 — smoke	—
False alarms / camera / 24 h	—
Latency (T4, TensorRT FP16)	—

Note: smoke mAP is always lower than fire mAP. Smoke has no crisp boundary — two expert annotators will not draw the same box. Report them separately.

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Where this is going

Three tiers sharing one evidence bus, detailed in PLAN_V2.md:

• SPARK — edge tier (RPi5 / Jetson Orin Nano), motion-gated inference
• BLAZE — commercial tier: tracking + a flame-flicker DFT verifier (real flames oscillate at 2–12 Hz; sunsets and traffic cones do not) + plume-growth verification, fused as log-odds per tracked fire
• INFERNO — server tier: multi-resolution ensemble, VLM adjudication, abstention, and distillation back into the smaller two

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Repository

FireGuard_Pipeline.ipynb      ← the notebook: data → training → export
build_pipeline_notebook.py    ← its generator (edit this, not the notebook)
fireguard_core.py             ← v0.1 inference engine: hazard state machine + overlays
FireGuard.ipynb               ← v0.1 inference demo
PLAN_V2.md                    ← three-tier architecture
DATA_AND_MODELS.md            ← model and dataset selection, with reasoning
DATASET_AUDIT.md              ← measured dataset facts and the eight traps
legacy/                       ← superseded two-notebook version

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License

MIT for this code. Datasets keep their own licenses (FASDD_CV is CC BY 4.0 — attribution required).

⚠️ The current pipeline builds on Ultralytics YOLO, which is AGPL-3.0. For a closed commercial product the same recipe should be ported to D-FINE or RF-DETR (both Apache-2.0). See DATA_AND_MODELS.md.

FireGuard is an assistive tool. Follow local fire-safety regulations; never deploy it as the sole life-safety system.

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فارسی

FireGuard یک سامانهٔ تشخیص آتش و دود برای دوربین مداربستهٔ داخلی است — خانه، دفتر، مغازه. نه برج جنگلی، نه پهپاد.

• دو کلاس و بس: 0 = fire · 1 = smoke
• یک نوت‌بوک: FireGuard_Pipeline.ipynb — از دانلود دیتاست تا خروجی TensorRT
• داده: FASDD_CV (۹۵,۱۲۶ تصویر، CC BY 4.0) + D-Fire (۲۱,۵۲۷) ≈ ۱۱۳ هزار تصویر
• هشت تله که همه‌شان اندازه‌گیری شدند و در خط لوله حل شده‌اند — مهم‌ترینشان اینکه نگاشت کلاس دو دیتاست دقیقاً برعکس هم است
• معیاری که مهم است: نرخ آلارم کاذب در هر دوربین در ۲۴ ساعت، نه فقط mAP

وضعیت: خط لولهٔ آموزش کامل و آزمایش‌شده است؛ اعداد دقت هنوز پر نشده‌اند.

نوت‌بوک را دستی ویرایش نکن — build_pipeline_notebook.py را عوض کن و دوباره بساز.