implementation_plan_component_purity_index.md

Implementation Plan: Edge "Component Purity & Cannibalization Index" (Updated)

This feature delivers real-time, on-device detection of PCB cannibalization (stripping/depopulation of valuable components like CPU sockets, gold edge fingers, and tantalum capacitors) to calculate a Board Integrity Score (BIS), generate vernacular voice warnings for scrap pricing adjustments, and propagate integrity data through the Dealer → Recycler chain to catch mid-chain stripping fraud.

Model training data collection is still in progress, so this plan is split into two tracks joined by a single frozen interface (model_contract.md): Track A builds and trains the detection model; Track B builds the full app pipeline against a mock model conforming to that same contract, so integration is not blocked on model readiness. Swapping the mock for the trained model later is a one-line config change, not a rewrite.

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User Review Required

• Labeling taxonomy refinement: Split desoldered footprints into forcibly_desoldered_pad (jagged, torn traces, burnt flux) and factory_unpopulated_pad (pristine silkscreen, smooth solder dots) to eliminate false cannibalization alerts on unpopulated board regions. Added occluded_region to prevent penalizing components covered by heatsinks/dust.
• Form-factor baselines: Replaced static class-wide component counts with form-factor sub-profiles (desktop_atx, desktop_micro_atx, server_1u, gpu_pcb, ram_dimm) in board_profiles.json to prevent small intact boards from receiving low BIS scores.
• Dynamic price floor: Replaced the rigid 35% hardcoded price floor with category-based scrap grade floors ($\text{Floor}_{\text{category}}$ = 15% for high-grade server/GPU boards up to 60% for low-grade power boards).
• Sequential edge execution: Adopted sequential model loading (dispose() calls between 21-class category classifier and YOLOv8 micro-model) to prevent memory crashes on 2GB–3GB RAM Android phones.
• BIS-delta tolerance bands: Updated recycler intake fraud threshold to $>12%$ for advisory flags and $>25%$ for critical alerts (up from 5%) to accommodate field-vs-warehouse camera & lighting variances.
• Offline voice fallback: Standardized pre-recorded .opus audio snippets via Service Worker CacheStorage as a fallback when Web Speech API offline regional voice packs are missing.
• Data sourcing strategy: Confirmed that existing open datasets can bootstrap the generic (*_intact) component classes, removing most of the annotation burden for those; the fraud-specific classes (forcibly_desoldered_pad, occluded_region) still require primarily field-staged photography, supplemented — not replaced — by mined web sources. See Phase 1, Component 2a. Requires a decision on whether forum/marketplace-sourced supplemental images are approved for use (see licensing note in Component 2a) before any scraping work begins.

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Track A: Micro-Component Detection Model

Phase 1: Data Collection & Labeling (Updated Guidelines)

Component 1: Refined Labeling Taxonomy (taxonomy/component_classes.yaml)

• [NEW] Defines fine-grained class states to eliminate false positives:
  • bga_cpu_socket_intact, bga_cpu_socket_missing
  • gold_edge_finger_intact, gold_edge_finger_stripped
  • tantalum_cap_intact, tantalum_cap_missing
  • ram_dimm_slot_intact, ram_dimm_slot_stripped
  • forcibly_desoldered_pad (jagged solder, torn traces, blowtorch/burnt flux residue — primary fraud signal)
  • factory_unpopulated_pad (clean silkscreen outline, smooth solder dots — unpopulated from factory, NOT fraud)
  • occluded_region (heatsinks, metal shields, heavy dirt/dust covering component pads)

Component 2: Data Collection & Sourcing Protocol (data_collection/)

• [NEW] capture_protocol.md — multi-angle capture protocol (Front side top-down, Reverse side top-down, and macro gold edge connector shot) to handle 2-sided PCB inspection.
• [NEW] board_sourcing_log.csv — logs board type, source, form factor (desktop_atx, micro_atx, server_4u, etc.), and intentional stripping state (pristine, blowtorched, organically degraded, factory unpopulated).
• Sourcing requirements: Must harvest dirty, corroded, dusty, and factory-unpopulated boards alongside pristine and stripped examples to match field scrap yard conditions.

Component 2a: Data Sourcing Strategy — Open Datasets, Mined Web Sources, and Field Photography (data_collection/sourcing_strategy.md)

Research into publicly available data confirms a three-tier sourcing strategy, since scraping alone cannot cover the fraud-specific classes:

Tier 1 — Open datasets, for bootstrapping *_intact classes (use directly, no scraping needed):

• A Dataset Ninja "PCB Component Detection" set — 1,410 images, 11,119 bounding-box-labeled objects across 9 classes (capacitor, transformer, MOSFET, resistor, etc.), released CC0 and explicitly tagged for the waste-recycling use case.
• HuggingFace PCB_COMPONENTS_LABELLED — 17-class YOLO-format set (IC, capacitor, connector, crystal oscillator, diode, ferrite bead, fuse, inductor, LED, resistor, switch, transistor, pins, pads, test points).
• HuggingFace pcb-object-detection-dataset — 21-class YOLO set including heatsinks and connectors.
• Roboflow Universe community PCB datasets (component-level bounding boxes, CC BY 4.0).
• Use: pretrain the detection backbone and pad out the *_intact and generic-component classes; re-map class names to taxonomy/component_classes.yaml. This does not reduce the need for field data on fraud-specific classes below, but meaningfully cuts the annotation volume needed for the "board still has its parts" side of the taxonomy.

Tier 2 — Mined web sources, as a supplemental seed set for forcibly_desoldered_pad only (not a primary source):

• Gold-recovery/e-waste hobbyist forums (e.g. Gold Refining Forum, finishing.com) contain user-posted photos of boards before, during, and after chip/pin removal, and active community discussion distinguishing "unpopulated" from "depopulated" boards — the closest organic analog to the forcibly_desoldered_pad class available online.
• Hobbyist "gold recovery" / heat-gun chip-pulling YouTube videos are a higher-yield variant of the same source: frame-extraction from a single video can produce many more usable images than a forum thread.
• Known limitations, by design not treated as a primary source:
  • Low volume per source and inconsistent photo quality/angle — doesn't match the phone-camera, scrap-yard conditions defined in capture_protocol.md.
  • No existing bounding boxes — still requires full manual annotation, same as field photos.
  • Licensing: forum and video content is user-generated and not freely licensed for reuse; scraping is low-risk for an internal/hackathon prototype but is not cleared for production training data without explicit permission or a licensed dataset. Flagged in User Review Required above — treat as blocked until a decision is made.
• Use: a small supplemental set to diversify what "stripped" looks like beyond staged photos, mined and annotated in parallel with, not instead of, Tier 3.

Tier 3 — Deliberately staged field photography, mandatory and primary for fraud classes (this is the existing Component 2 protocol above):

• Only source that guarantees: domain match to real collector phone conditions, controlled multi-angle capture, and clean separation between forcibly_desoldered_pad, factory_unpopulated_pad, and occluded_region at capture time (know the ground truth because you staged it, rather than inferring it after the fact from a forum photo).
• Remains the dataset of record for Phase 4 evaluation thresholds; Tier 1/2 data is for pretraining and augmentation only, not held-out test data.

Component 3: Annotation & QA

• [NEW] Bounding-box annotation via CVAT or Label Studio, exported in COCO/YOLO format.
• [NEW] qa_annotation_review.py — inter-annotator agreement check (min. 2 annotators per image, flag disagreement >10% of boxes for adjudication).
• Target: minimum 300 images per class, with at least 30% deliberately-stripped/burnt negative examples, before first training run.

Phase 2: Form-Factor Baseline Definitions

Component 4: Dynamic Board Profiles (baselines/board_profiles.json)

• [NEW] Defines expected component counts/area ratios per board class and form factor:
  • desktop_atx, desktop_micro_atx, desktop_itx
  • server_1u_2u, server_blade_4u
  • gpu_pcb_high_end, gpu_pcb_budget
  • ram_dimm_desktop, ram_so_dimm_laptop
• Sourced from recycling domain experts — consumed downstream by purity_assay.py.

Phase 3: Model Training

Component 5: Training Pipeline (training/train.py)

• [NEW] YOLOv8n/s fine-tuning with multi-scale training (imgsz=960 or 1280) and custom anchor ratios for elongated gold_finger boxes ($>4:1$).
• Heavy augmentations for field realities: specular glare on gold fingers, lighting variance, dirt noise, and angle tilt.
• [NEW] train_config.yaml — Focal loss & elevated class weights for forcibly_desoldered_pad vs factory_unpopulated_pad.

Component 6: Auxiliary Desoldered Pad Classifier Head

• [NEW] Secondary classification head specifically trained to distinguish desoldered blowtorch pads from factory empty pads.

Component 7: Export & Quantization (export/export_tflite.py)

• [NEW] Export to TFLite INT8 quantized, target size <15MB, target inference <1s on low-end Android hardware.
• [NEW] model_contract.md — freezes input/output tensor schema.

Phase 4: Evaluation

Component 8: Model Evaluation (eval/evaluate.py)

• [NEW] Per-class mAP, plus dedicated fraud recall on forcibly_desoldered_pad and false-positive rate on factory_unpopulated_pad.
• Thresholds: $\ge 90%$ recall on intact classes, $\ge 85%$ recall on desoldered pad classes, and $\le 5%$ false positive rate on factory unpopulated pads.

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Track B: Integration into Kabadiwala Connect

Component 9: Purity Engine & Memory-Conscious Orchestrator (purity_engine/)

• [NEW] purity_engine/model_client.py — wrapper supporting MockModelClient and TFLiteModelClient with explicit tensor memory management (model.dispose() between Stage 1 and Stage 2).
• [NEW] purity_engine/purity_assay.py — implements calculate_board_integrity_score(detected_components, form_factor):
  • Dynamic price formula: $$\text{Adjusted Price} = \text{Base Price} \times \left( \text{Floor}{\text{category}} + (1 - \text{Floor}{\text{category}}) \times \frac{\text{BIS}}{100} \right)$$
  • Scrap Grade Floors: Server/GPU = 15%, Desktop = 30%, Low-grade Power = 60%.
• [NEW] purity_engine/pcb_analyzer.py — single entry point for Collector Lite & Recycler Portal.

Component 10: Vernacular Voice Alerts with Audio Fallback (purity_engine/vernacular_voice.py)

• [NEW] Extends Collector Lite's 8-language Web Speech API audio system with Service Worker pre-recorded .opus audio fallbacks for offline devices lacking regional voice packs.
• Message templates parameterized by BIS band (Pristine $\ge 85%$, Partial $50\text{--}84%$, Severe $<50%$).

Component 11: Collector Lite Multi-Angle UI Integration (mobile/kabadiwala-lite.html)

• Extend Step 1 of Collector Lite: Stage 1 21-class classifier detects PCB $\rightarrow$ prompts guided Front/Back photo capture $\rightarrow$ triggers pcb_analyzer.
• Displays BIS score, missing-component breakdown, and transparent price adjustment in Step 3.
• Persists assay JSON into IndexedDB with the scrap slip.

Component 12: QR Handover Payload Enrichment (mobile/dealer.html, backend)

• QR payload includes collector BIS score, form factor, and assay report hash.

Component 13: Recycler Portal BIS-Delta View (recycler-dashboard-frontend/)

• Re-scan intake lot via pcb_analyzer.
• Tolerance Bands:
  • $< 12%$ BIS Drop $\rightarrow$ Normal capture variance (Pass).
  • $12\text{--}25%$ BIS Drop $\rightarrow$ Discrepancy Flagged.
  • $> 25%$ BIS Drop $\rightarrow$ Critical Fraud Alert & CPCB Form 6 PDF Audit Log Entry.

Component 14: Backend API Changes (backend/)

• [NEW] POST /api/lots/{lot_id}/assay & GET /api/lots/{lot_id}/bis-delta.

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Sequencing Summary

	Track A (Model & Dataset)	Track B (App Integration)
Now	Tier 1: bootstrap from open datasets. Tier 3: harvest dirty/stripped/unpopulated boards; annotate fine-grained taxonomy. Tier 2 (mined web sources) blocked pending licensing decision	Build multi-angle UI & pricing engine against MockModelClient
Parallel	Multi-scale YOLOv8 training with focal loss & TFLite INT8 quantization	Collector Lite, audio fallbacks, Recycler Portal BIS-delta tolerance UI
Hand-off	Ship model clearing Phase 4 thresholds	Swap TFLiteModelClient in with zero code changes