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.
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.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Image Processing
Apply image processing to solve real industry problems and demonstrate production-level capability.
- Edge Detection
Apply edge detection to solve real industry problems and demonstrate production-level capability.
- Opencv
Apply opencv to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Documentation
Apply documentation to solve real industry problems and demonstrate production-level capability.
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