Visual Inventory Counting

AI-powered inventory counting using cameras and mobile devices to reduce manual counting time by 80%.

Counting Methods

Method	Use Case	Accuracy	Time
Shelf Scan	Bottles, cans, boxes	±3%	5 sec/shelf
Bin Count	Loose items (produce)	±10%	3 sec/bin
Pallet Count	Bulk storage	±5%	10 sec/pallet
Phone Scan	Walk-through inventory	±5%	30 min total

How It Works

┌─────────────────────────────────────────────────────────────────┐
│  VISUAL INVENTORY COUNT                         Walk-In Cooler  │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │                                                          │   │
│  │         [Live Camera Feed - Phone/Tablet]               │   │
│  │                                                          │   │
│  │    ┌─────────┐  ┌─────────┐  ┌─────────┐               │   │
│  │    │Chicken  │  │ Beef    │  │ Salmon  │               │   │
│  │    │ 24 lbs  │  │ 18 lbs  │  │ 12 lbs  │               │   │
│  │    └─────────┘  └─────────┘  └─────────┘               │   │
│  │                                                          │   │
│  │    AR Overlay shows detected items and counts           │   │
│  │                                                          │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                  │
│  SCANNING PROGRESS                                              │
│  ───────────────────────────────────────────────────────────    │
│  Walk-In Cooler:   [████████████░░░░░░░░]  65%               │
│  Items Counted:    24 / 38                                     │
│  Time Elapsed:     8 min 23 sec                                │
│  Est. Remaining:   4 min                                       │
│                                                                  │
│  [Pause] [Skip Zone] [Complete Section]                        │
└─────────────────────────────────────────────────────────────────┘

Model Architecture

Component	Model	Purpose
Object Detection	YOLOv8-L	Item detection
Instance Segmentation	SAM	Overlapping items
Counting Network	CSRNet	Dense counting
AR Overlay	ARKit/ARCore	Real-time visualization

Inventory Scan Workflow

API: Start Visual Count

POST /api/v1/vision/inventory/count
Authorization: Bearer {access_token}
Content-Type: application/json

{
  "location_id": "loc-xyz789",
  "zone": "walk_in_cooler",
  "device_id": "device-001"
}

API: Get Count Results

GET /api/v1/vision/inventory/count/{session_id}
Authorization: Bearer {access_token}

{
  "session_id": "count-abc123",
  "zone": "walk_in_cooler",
  "status": "completed",
  "items": [
    {
      "item_id": "inv-chicken",
      "name": "Chicken Breast",
      "detected_quantity": 24.5,
      "unit": "lb",
      "confidence": 0.94,
      "previous_count": 28.0,
      "variance": -3.5
    }
  ],
  "duration_seconds": 485,
  "accuracy_score": 0.96
}

Benefits

Metric	Manual Count	Visual Count	Improvement
Time	2.5 hours	30 minutes	80% faster
Accuracy	±5-10%	±3-5%	2x more accurate
Frequency	Weekly	Daily	7x more frequent
Labor Cost	$75/count	$15/count	80% savings