julian
e4827c9651
Research note from the recognizer-options query. fast-alpr v0.4.0 (MIT) — a swappable YOLOv9-detector + CCT-OCR pipeline on ONNX Runtime, CPU-only and offline — fits the decided vision-service architecture and is MIT end-to-end (code + published weights), so the ANPR path may not need the scoped AGPL exception. Flags the open caveats: verify model-weight provenance, and benchmark AL-plate accuracy (default global vs. the 40+ country EU model). fast-alpr is plate-only, so the vehicle-verification job stays ours to build. Decision kept open. Updates opencv-anpr-service (new "Recognizer evaluation" section + licensing nuance), vision-service (open/next), index, log. Claude-Session: https://claude.ai/code/session_01Xcm6ikLgGoCxxHrxtjkk5V
68 lines
4.0 KiB
Markdown
68 lines
4.0 KiB
Markdown
---
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type: decision
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tags: [parking, decisions, vision, anpr, anti-fraud]
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sources: []
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updated: 2026-06-15
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status: open
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---
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# Decision: Host-side Vision Service (ANPR + vehicle verification)
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Taken 2026-06-15, as part of the business-layer build ([[session-model]]).
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## Decisions
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1. **Build a host-side vision service** ([[opencv-anpr-service]]) that does ANPR (plate → identity)
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**and** vehicle-attribute verification (anti-spoofing witness) on snapshots from ordinary
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Hikvision/Dahua cameras.
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2. **It replaces the dedicated edge-AI [[lpr-camera]]** as the recognition path: ordinary IP cam →
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snapshot (`Snapshot.bytes`, already pulled by the camera driver) → vision service → plate +
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vehicle. Removes the special LPR camera from the [[bom]] as a requirement (still allowed as an
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option).
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3. **Deployment: a separate local Python/OpenCV microservice** on the appliance, called over
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**localhost HTTP** by the Node backend. Fully offline ([[offline-first]]); its own process and
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failure domain; the host falls back to the ticket path if it's unavailable.
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4. **Licensing exception:** AGPL components (e.g. YOLO plate/vehicle models, OpenALPR) are
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**permitted inside this service only**, because it's a separate process not linked into the app —
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the app stays strictly MIT/Apache/BSD. Amends [[standing-decisions]].
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5. **Recognition is advisory, evidence is authoritative.** A read never single-handedly authorizes
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a paid/access barrier open; it flags for [[reconciliation]] and attaches (with the source image)
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to the signed [[append-only-event-chain]] entry. Low confidence → fallback, never strand a car
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([[fail-state-safety]]).
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## Why
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- **Replace vs. edge-AI camera:** host-side recognition on cheap IP cams shifts cost from per-lane
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smart cameras to one compute box + our software; gives us the raw image for the second job below.
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- **Vehicle verification is the real prize (user-driven, 2026-06-15):** plate-only ANPR can't catch
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a **printed/spoofed plate on a different car**. Extracting vehicle attributes/fingerprint lets the
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system reconcile *the car*, not just the number — directly filling the independent-witness gap the
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[[append-only-event-chain]] calls out as unbuilt.
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- **Separate-process + AGPL-scoped** keeps the app's permissive-license guarantee intact while not
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crippling accuracy (the strict permissive-only ANPR path is markedly weaker — that tradeoff was
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weighed and the scoped exception chosen).
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## Rejected / alternatives
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- **Strict permissive-only ANPR in-app** — license-clean but weaker accuracy and more build; the
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separate-process AGPL exception was chosen instead.
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- **Keep the edge-AI LPR camera as primary** — viable fallback if host-side accuracy disappoints;
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not chosen now, kept on the table in [[opencv-anpr-service]].
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- **Embed OpenCV in Node** (opencv4nodejs/WASM) — rejected: native-build pain, weaker model
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ecosystem, no process isolation, shares the app's failure + license surface.
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## Open / next
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- Recognizer selection: **fast-alpr (MIT, YOLOv9 detector + CCT OCR on ONNX Runtime) is the evaluated
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baseline** as of 2026-06-19 — fits this decision's shape (separate offline localhost process,
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swappable) and is permissive end-to-end, so the ANPR path may not need the AGPL exception (pending a
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model-**weight-provenance** check). Plate-only: it does NOT cover the vehicle-verification job. Open:
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the provenance check + an accuracy benchmark on real Albanian plates (default global vs. the 40+
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country EU model). See [[opencv-anpr-service]] "Recognizer evaluation".
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- Vehicle-model selection + fingerprint method + anomaly threshold (the Job-2 anti-spoofing stage,
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still ours to build on the same ONNX runtime) ([[opencv-anpr-service]]).
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- Appliance compute footprint (CPU vs. small GPU/NPU) — [[bom]] / [[open-questions]].
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- Service API + the Node-side adapter; per-camera opt-in wiring.
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- Reconciliation logic that consumes plate+vehicle witness vs. commanded opens (still unbuilt — see
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[[append-only-event-chain]], [[reconciliation]]).
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