Headlight defect detection: Datasets & Models

Manual headlight inspection is slow, subjective, and hard to scale across shifts, and subtle defects like fine cracks or early condensation are easy for a tired human eye to miss. Automating this with computer vision gives consistent, repeatable inspection at line speed and creates an auditable record of every unit checked. For automotive suppliers under strict quality requirements, catching a defective headlight before it ships avoids costly warranty claims and rework downstream. A detection model pinpoints exactly where the defect is, which speeds up root-cause analysis on the plant floor.

An RF-DETR detection model delivers accurate defect localization while integrating with the cameras and inspection stations you already have on the line. It scales across multiple stations and product variants without adding headcount, and every inspection is logged for traceability. Starting from an existing headlight defect dataset lets teams reach a working prototype quickly and iterate as new defect types appear.

1. Upload an image: Drop any photo into the widget above to try headlight defect detection for free, right on this page.
2. Preview instant results: The model detects headlight assembly, cracked lens, condensation, lens scratch, broken clip 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.
3. 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.
4. 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.