Bag Depalletizer

AI vision that finds, classifies and localises bags on real-world pallets, so a robot can unload them.

The Captic Bag Depalletizer gives a robot the eyes to unload bags from pallets. A 3D camera looks at the pallet, the AI finds and classifies every bag, and the system sends the pose of the next bag to pick to your robot or PLC.

Why bags are hard

Bags are one of the hardest things to depalletize automatically. They are deformable, so no two look the same. Pallets are messy: bags shift, sag and overlap, stacking patterns change, and lighting varies across the day. Rule-based vision systems and DIY vision toolkits struggle with this variability, which slows down projects and drives up costs.

What it does

• Finds every bag, on any pallet. The AI adapts to deformable bags, changing light and pallet variability. You don't need to configure stacking patterns, layer heights or layouts in advance.

• Estimates the pick pose. For each bag, the system calculates where and at what angle the robot should pick it.

• Classifies bags. Built-in classification tells bag types apart and can recognise damaged or deformed bags, for example to route them for manual inspection.

• Speaks your robot's language. Poses are sent in the coordinate convention of your robot brand, with support for 35 brands. See Coordinate Conventions.

• Keeps operators in control. In Human Mode, an operator validates each result before it's sent, and the Captic team can review and override results remotely.

At a glance

Cycle time	Under 1.5 s per bag
Bag weights	5 to 50 kg
Bag materials	Paper, plastic, woven polypropylene and multi-layer bags
Robots	35 brands, natively or through a PLC or OPC UA. See Robots.
Output	Pick pose (X, Y, Z and rotation) and bag class

How it works

1. The 3D camera captures the pallet.

2. The AI detects the bags, classifies them and estimates each pick pose.

3. Detections outside the Volume of Interest, the area the robot can reach, are ignored.

4. The pose of the next bag is sent to the robot or PLC through the Integration Flow.

5. The robot picks the bag, and the cycle repeats.

Setting up

Follow Getting Started to go from delivery to the first pick.

Key settings

• Confidence score: how confident the AI must be before a bag is picked.

• Model selection: the AI model for your bags and pallets.

• Volume of Interest: the 3D area in which bags are considered for picking.

• Coordinate convention: matches the output to your robot brand.

Need something tailored to your line? Contact [e-mail].