Screw detection: Datasets & Models
Screw detection uses computer vision to locate and count screws in images of assembled products, fastener trays, and production lines. It flags missing, misaligned, or incorrectly seated screws during manufacturing and quality control.
Screw detection
This detection workflow locates and counts individual screws in images from inspection cameras, handheld scanners, or fixed line-side stations, returning bounding boxes with position and count data. Built on a screw dataset spanning varied head types, sizes, finishes, and orientations, it identifies each fastener so downstream logic can flag missing or misplaced screws during assembly and QC.
Try it out
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Why automate screw detection
Manual screw inspection is slow, tedious, and error-prone: operators scanning every unit for a missing or cross-threaded fastener will inevitably miss defects, especially on high-throughput lines. Automating screw detection with computer vision gives consistent, repeatable checks on every part without fatigue, catching missing or misplaced screws before products move downstream. In manufacturing and QC, that means fewer field failures, less rework, and objective inspection records you can audit.
Key benefits
An RF-DETR model can be trained on your own screw dataset and deployed against existing inspection cameras, so you don't need to rebuild your line to add automated verification. You get accurate fastener counts and localization that scale across multiple stations and product variants, with detection results that integrate cleanly into PLCs, MES, or reporting dashboards. As you collect more images, the model improves and adapts to new part types.
How to use it
- Upload an image: Drop any photo into the widget above to try screw detection for free, right on this page.
- Preview instant results: The model detects screw, screw head, screw hole, bolt, washer 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 screw detection
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 screw detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Screw Detection project to Roboflow Agent.