LISA: The Bed Sheet Superhero
AI vision + automation delivering fatigue‑free, high‑throughput, fully logged linen inspection & sorting.
Sakar Robotics
September 24, 2024
Introduction
LISA (Linen Inspection & Sorting Automation) applies production‑grade computer vision and deterministic routing to guarantee consistent, hygienic bedroll supply.
Core Problem Statement
Manual inspection scales linearly with labor, suffers from attention decay, and lacks structured data for optimization.
Functional Architecture
| Tier | Component | Purpose |
|---|---|---|
| Sensing | Multi‑camera + controlled illumination | Uniform acquisition |
| Inference | CV models (stain, tear, texture anomaly) | Defect detection |
| Decision | Policy evaluator | Grade & route choice |
| Actuation | Diverter & conveyor logic | Physical segregation |
| Data | Event & metrics store | Analytics & audit |
| Interface | Operator console | Oversight & tuning |
Capabilities
- Multi‑defect classification (stain severity, tear geometry)
- Configurable acceptance thresholds per division
- Auto reject lane / recirculation logic
- Batch & item lineage traceability
- Performance dashboard (throughput, defect rate, grade distribution)
Comparative Value
| Aspect | Legacy Manual | LISA Automated | Improvement Vector |
|---|---|---|---|
| Consistency | Variable | Stable | Model invariance |
| Throughput | Fatigue constrained | Continuous | Parallelism |
| Data Availability | Sparse / anecdotal | Granular & structured | Logging layer |
| Missed Defects | Higher | Lower | Precision inference |
| Training Overhead | Recurrent | Low (UI + SOP) | Standardization |
Operational Metrics Framework (Template)
| KPI | Definition | Optimization Lever |
|---|---|---|
| Defect Detection Precision | TP / (TP + FP) | Model threshold tuning |
| Cycle Time / Item | Entry → graded | Conveyor & inference latency |
| Rework Ratio | Re-screened / total | Policy calibration |
| Grading Consistency Index | Std dev across shifts | Lighting + model versioning |
| Utilization | Active run / scheduled window | Preventive maintenance |
Extension Pathways
1. Additional textile SKUs (blankets, pillow covers)
2. Edge model personalization per depot
3. Predictive defect clustering analytics
4. API federation into centralized quality BI
Implementation Stages
| Stage | Goal | Exit Criteria |
|---|---|---|
| Assessment | Baseline & sample capture | Valid dataset curated |
| Pilot | Shadow run & threshold set | >95% detection vs gold set |
| Rollout | Full switch & training | SLA adherence ≥ target |
| Optimize | KPI uplift & drift watch | Stable metrics 3+ cycles |
LISA transforms textile hygiene from manual assurance to an instrumented, data‑augmented process layer.