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Edge Detection Pipeline for a Manufacturing QA Camera

FreeVerified credential1 weekBeginner

Overview

What this challenge is about.

Build an OpenCV edge-detection pipeline to count burrs on car parts and compare results to ground truth. Earn a verifiable certificate.

The scenario

The supplier (around 1,200 staff across two German plants) ships around 500k parts/month; reducing false-fail rate by 30 percent saves roughly EUR 80k/year in rework labor.

CredentialBlockchain-anchored
ShareableLinkedIn-ready
LanguageEnglish
PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Build a lighting-robust classical edge-detection pipeline that counts burrs within ±1 of human-annotated ground truth and document when a deep-learning approach would be worth the upgrade.

Earning criteria — what you'll demonstrate

  • Apply classical image-processing operations to a real QA problem
  • Reason about when classical methods beat deep learning
  • Evaluate a vision pipeline against human-annotated ground truth
  • Document a vision pipeline so it survives lighting changes

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Computer Vision (Undergraduate)

Bachelor · Computer Vision

Strong alignment

This challenge maps to Computer Vision (Undergraduate) at the Bachelor level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Computer Vision Engineer

Classical CV pipelines on real factory data are a daily portion of CV-engineer work in manufacturing-AI roles.

This challenge sharpens

  • image-processing
  • edge-detection
  • opencv

AI Engineer

Reasoning about when classical methods are sufficient (instead of jumping to deep learning) is the AI-engineer judgment that saves teams months of work.

This challenge sharpens

  • image-processing
  • documentation
  • opencv

Machine Learning Engineer

Robust evaluation against ground truth across operating conditions is the MLE habit production teams rely on.

This challenge sharpens

  • evaluation
  • opencv
  • edge-detection

One more thing

You can put a credential on your CV by Friday.