Compare Stereo Depth Methods for a Drone Inspection Startup
Overview
What this challenge is about.
Compare three stereo depth methods on drone turbine data, measure accuracy and speed, then recommend one. Get a verifiable certificate.
The scenario
The startup (around 25 people, 90 wind farms served across France, Spain, and Portugal) processes about 12 turbines per drone-day; a 2x improvement in depth accuracy near blade tips would meaningfully cut their false-defect rate.
The Brief
What you'll do, and what you'll demonstrate.
Pick the best stereo depth method for blade inspection by trading off accuracy, edge-case robustness, and on-device runtime.
Earning criteria — what you'll demonstrate
- Implement and compare classical vs. learning-based stereo depth
- Quantify accuracy with standard metrics (D1, MAE) and edge-aware metrics
- Reason about the accuracy/latency/memory trade-off for edge deployment
- Defend a methodology choice in writing to a technical audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Stereo Depth Estimation
Apply stereo depth estimation to solve real industry problems and demonstrate production-level capability.
- Multi View Geometry
Apply multi view geometry to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Edge Deployment
Apply edge deployment to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking 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: