Skip to content

St. John's, Newfoundland.

Engineering casebook

Case 04 / Machine learning & inspection

Wind-turbine defect detection

A computer-vision workflow for finding candidate defects in turbine imagery, with training metrics, validation figures and an image-by-image prediction explorer.

100 training epochs · saved validation and predictions
Machine learningScientific Python
The engineering question

How can a computer-vision model help reviewers find candidate defects in large collections of turbine imagery?

I built a YOLOv8 inspection workflow covering image preparation, model training, inference and visual review. The explorer displays saved prediction coordinates, while the training curves and validation figures show the detector’s performance on the project dataset.

Inside the method

Method and workflow

  1. 01

    Prepare and label

    Define dirt and damage classes, organize the imagery and inspect the annotations.

  2. 02

    Train the detector

    Fine-tune the model across 100 epochs and record validation metrics rather than relying on a few attractive predictions.

  3. 03

    Read the errors

    Compare precision, recall, mAP and the confusion matrix. Inspect missed defects and false positives.

  4. 04

    Review predictions

    Adjust the confidence threshold and examine individual candidate boxes on the original images.

Inspect the evidence

Look closer.

Original turbine inspection sample 1damage 86%dirt 71%
2 candidate boxes

Saved model predictions, filtered interactively. Confidence is a model score; these boxes are not verified defect labels.

Training / validation

What changed over 100 epochs?

The lines come from the saved training CSV. Select an epoch to inspect its validation metrics.

82.3%mAP5080.5%Precision78.9%Recall
10.501100 epochs
mAP50mAP50–95
Project figures / 4 selected figures

Project figures

01 / 04
The run’s normalized confusion matrix
The run’s normalized confusion matrix
The run’s normalized confusion matrix

Saved validation confusion matrix from the same 100-epoch training run used by the learning-curve explorer.

Open full-size image ↗
Technical notes

Model assumptions and scope

  • The 82.3% mAP50 figure describes a saved validation run, not an independent deployment benchmark.
  • Images containing detections are not the same as correctly classified images.
  • Predictions assist inspection review; they do not establish structural condition or replace engineering assessment.
Potential extensions

Evaluate on a held-out turbine or inspection sequence and report class-level errors and inference hardware.

Have a related problem?

Let’s work through it.

Robotics, simulation and control consultation across Canada, with remote support available internationally. Share your system and the result you need.

Request consultation
Next case / Image processing & mappingOverlapping imagery to a stitched mosaic