Paint defect detection: Datasets & Models
Automated visual inspection that finds paint defects like runs, sags, orange peel, and scratches on painted surfaces using object detection. Runs on line cameras or inspection-station images to flag flawed parts before they move downstream.
Paint defect detection
This detection workflow locates paint defects on coated parts and draws bounding boxes around flaws such as runs, sags, drips, bubbles, orange peel, dust nibs, and scratches. It accepts still images or video frames from paint line cameras or QC inspection stations, and you can train it on a paint defect dataset to flag defective parts before they move downstream.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate paint defect detection
Manual paint inspection depends on human eyes under booth lighting, where subtle defects are easy to miss and inspector fatigue leads to inconsistent calls across shifts. In automotive, appliance, and metal fabrication QC, a missed run or contamination can mean costly rework or a warranty claim after the part ships. Automating this with detection gives you consistent, repeatable inspection at line speed, catches defects earlier in the process, and produces a traceable record of every flagged part for root-cause analysis.
Key benefits
You get objective, frame-by-frame defect classification that holds a fixed standard across every shift and every color batch, integrated with the machine-vision cameras already installed on your paint line. The workflow scales from a single inspection station to multiple lines without adding headcount, and the labeled defect data you collect feeds continuous model retraining as new finishes and part geometries are introduced.
How to use it
- Upload an image: Drop any photo into the widget above to try paint defect detection for free, right on this page.
- Preview instant results: The model detects paint run, scratch, bubble, dust nib, orange peel 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 paint defect 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 paint defect detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Paint Defect Detection project to Roboflow Agent.