memari-majid/Wind-Turbine-Blade-Defect-Detection-with-YOLO-Models

Defect Detection with YOLO Family Models

35
/ 100
Emerging

This project helps wind turbine operators and maintenance teams automatically identify critical defects on turbine blades from high-resolution inspection images. It takes raw images of wind turbine blades as input and outputs precise locations and classifications of various blade defects, including very small and subtle ones. The primary users are field technicians, inspection engineers, and asset managers responsible for wind farm maintenance.

No commits in the last 6 months.

Use this if you need to automate and improve the accuracy of wind turbine blade defect detection, especially for small and hard-to-spot imperfections that are critical for timely maintenance.

Not ideal if you are looking for a general-purpose object detection tool for other industries or if your primary concern is real-time, extremely low-latency detection on limited computational resources.

wind-energy turbine-maintenance asset-inspection quality-control predictive-maintenance
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 14 / 25

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Stars

13

Forks

3

Language

Python

License

MIT

Last pushed

Nov 14, 2024

Commits (30d)

0

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