Component presence check: Datasets & Models
Automatically verify that every required part is present on an assembled product before it moves down the line. This detection workflow flags missing components in real time using images from existing inspection cameras.
Component presence check
This detection workflow inspects assembled products and identifies missing components by locating each expected part, such as screws, connectors, brackets, clips, and labels, then comparing the detected parts against the unit's bill of materials. It takes images from existing inspection station cameras as input and runs on a model trained with a missing component dataset to flag incomplete assemblies before they move down the line.
Try it out
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Why automate component presence check
Manual presence verification in manufacturing and QC is slow, inconsistent, and prone to fatigue, an operator scanning hundreds of near-identical boards or housings per shift will inevitably miss the occasional absent fastener or unseated connector. A missing part that escapes inspection turns into rework, warranty claims, or field failures that cost far more to fix downstream. Automating the check with a detection model gives you a consistent, auditable verdict on every single unit rather than a sampled subset. It catches omissions the moment they happen, so defective assemblies never reach packaging.
Key benefits
You get 100% inspection coverage with objective, repeatable results and a visual record for traceability and root-cause analysis. The workflow runs on cameras you likely already have at the line and integrates with PLCs or MES to trigger reject gates and alerts. As you add new product variants, you extend the dataset and retrain rather than re-engineering the whole station, so the system scales across SKUs and plants.
How to use it
- Upload an image: Drop any photo into the widget above to try component presence check for free, right on this page.
- Preview instant results: The model detects screw, connector, bracket, clip, label 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 component presence check
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 component presence check, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Component Presence Check project to Roboflow Agent.