How can computer vision help a bottling or filling line catch underfilled containers and bad caps?

---
title: "How to check bottle fill levels and caps with computer vision"
description: "A camera after the filler and capper can image every bottle, jar or can as it passes. A computer vision model trained on your own containers can check the fill height against the target line, and spot missing, crooked or loose-looking caps, missing seals and damaged containers. It can signal a reject gate or alert the line operator, and it keeps a photo of each rejected item so the cause can be traced to a filler head or capper. Line staff check rejected items, adjust the equipment and decide when to stop the line, and your usual quality checks stay in place."
url: https://lidvizion.ai/answers/how-to-check-bottle-fill-levels-and-caps-with-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

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# How can computer vision help a bottling or filling line catch underfilled containers and bad caps?

A camera after the filler and capper can image every bottle, jar or can as it passes. A computer vision model trained on your own containers can check the fill height against the target line, and spot missing, crooked or loose-looking caps, missing seals and damaged containers. It can signal a reject gate or alert the line operator, and it keeps a photo of each rejected item so the cause can be traced to a filler head or capper. Line staff check rejected items, adjust the equipment and decide when to stop the line, and your usual quality checks stay in place.

## What this means in practice

- Place a camera and steady lighting just after the filler and capper.
- Label images of good containers and each fault type for every product and size.
- Train or tune a model and connect it to an alert or reject gate.
- Line staff review rejected items and adjust the filler or capper as needed.
- Track rejects by filler head and capper so recurring faults get fixed at the source.

## How Lid Vizion approaches it

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