---
license: cc-by-4.0
tags:
  - object-detection
  - yolov8
  - wind-turbine
---

# Wind turbine blade damage detection — YOLOv8s

Fine-tuned from `yolov8s.pt` (COCO) on the WTBD dataset.

## Classes

craze, corrosion, surface_injure, thunderstrike, crack, hide_craze

Class indices are stored in the checkpoint as `model.names` — read them from
there rather than hardcoding, so a reordered `data.yaml` cannot mislabel
predictions.

## Training

- 100 epochs, imgsz=1024, seed=42, batch auto
- Ultralytics 8.3.40

## Metrics (held-out test split)

mAP50: 0.805 — mAP50-95: 0.503

## Data source

Ji, L., Cheng, J., Wu, S. *Multiclass Dataset for Intelligent Detection of
Wind Turbine Blade Defects Using Drone Imagery.* Scientific Data 13, 396 (2026).
https://doi.org/10.1038/s41597-026-06762-x