Logistics and Warehousing

Warehouses and logistics operations generate enormous volumes of images: receiving docks, conveyor belts, shelf cameras, loading bays. Most of these images go unreviewed. When something goes wrong, the team finds out too late.

Datature Vi trains AI models on your own warehouse photos so they can spot problems in real time. Crushed packages on the conveyor, wrong products in a shipment, empty shelf slots that should be full. The model learns from examples you provide, then watches your camera feeds and flags issues as they happen.

No data science team is needed. If your warehouse team can take photos and describe what they see, that is enough to get started.

For an interactive overview of this application, visit the warehouse intelligence use case on vi.datature.com.

Common applications

Damaged goods detection

What you need

• 50–150 images of packages on your conveyor belt or receiving dock
• At least 20–30 images showing actual damage (crushed corners, water damage, torn packaging)
• Consistent camera angle matching your production setup

Task type: VQA

Use Visual Question Answering with a standard question across all images:

For automated pipelines, combine with structured data extraction to return JSON:

{
  "condition": "damaged",
  "damage_type": "crush",
  "location": "top right corner",
  "severity": "high"
}

Task type: Phrase Grounding

Use Phrase Grounding if you need bounding boxes around the damaged area, for example to crop and attach to a damage report:

• Annotate each damaged image by drawing a box around the damage and labeling it: "crush damage", "water damage", "torn corner"
• At inference, the model returns bounding box coordinates you can use to highlight the damage in your dashboard

Inventory counting

What you need

• Images of your shelves, pallets, or bins
• Annotations that state the count of target items

Task type: VQA

Train a VQA model with count-based questions:

Shipment verification

Verify that delivered goods match what was ordered. This is useful for receiving dock automation.

Task type: VQA

Train a model on images of your common products with verification questions:

Structured output for automated receiving

Use structured data extraction to integrate with your receiving system:

{
  "product_match": false,
  "detected_product": "XR-150",
  "expected_product": "XR-200",
  "action": "hold_for_review"
}

Label reading

Read shipping labels, SKU codes, or product identifiers from images taken at variable angles and lighting conditions.

Task type: Freeform Text / VQA

Train on images of your label formats with extraction prompts:

For multiple fields from one label, use structured data extraction:

{
  "tracking_number": "1Z999AA10123456784",
  "destination_zip": "94102",
  "weight_kg": "2.3",
  "service_class": "priority"
}

Training tips for logistics

Capture images in real conditions: warehouse lighting, motion blur from conveyors, and product orientation variation should all appear in your training data.

Include negative examples: for damage detection, include plenty of undamaged package images. For counting, include empty shelves.

Use consistent prompts: the same question phrasing should be used across all annotations and at inference. Changing the prompt wording can reduce accuracy.

Start small: run a first training pass with 50–100 images, test it on your real environment, then expand your dataset to address specific failure cases.

Next steps