Fill level inspection: Datasets & Models
Computer vision system that inspects containers on a production line and detects whether each is filled to the correct level. It flags underfills, overfills, and empty containers before they leave the fill station.
Fill level inspection
This workflow runs fill level detection on containers moving through a production line, using an object detection model to locate each bottle, jar, or can in camera frames and measure its product line against the expected fill zone. It classifies each unit as correctly filled, underfilled, overfilled, or empty, taking still images or video frames from line cameras as input.
Try it out
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Why automate fill level inspection
Manual fill checks depend on operators visually spot-checking a fraction of units, which misses defects at line speed and produces inconsistent results across shifts. Automating fill level detection with computer vision inspects every container, catches underfills and overfills in real time, and creates a consistent audit trail for quality control. For manufacturing and QC teams, that means fewer customer complaints over short fills, less product giveaway from overfilling, and the ability to divert defective units before they reach packaging.
Key benefits
You get 100% inspection coverage instead of sampling, faster reaction to fill faults, and objective pass/fail decisions that hold up across lines and shifts. The models run on frames from the cameras you already have, integrate with existing reject and PLC systems, and scale from a single fill station to plant-wide deployment as your volume grows.
How to use it
- Upload an image: Drop any photo into the widget above to try fill level inspection for free, right on this page.
- Preview instant results: The model detects bottle, jar, can, fill line, cap within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for fill level inspection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for fill level inspection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Fill Level Inspection project to Roboflow Agent.