Introduction to YOLO
YOLO (You Only Look Once) has revolutionized real-time object detection, making it perfect for industrial defect detection applications where speed and accuracy are critical.
Why YOLO for Defect Detection?
- Real-time Processing: 30+ FPS on modern GPUs
- Single-stage Detection: Fast and efficient architecture
- High Accuracy: State-of-the-art results on various datasets
- Easy to Train: Transfer learning from pre-trained models
Setting Up Your Environment
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# Install required packages
pip install ultralytics opencv-python numpy
# Import libraries
from ultralytics import YOLO
import cv2
Training Your First Model
1. Prepare Your Dataset
Organize your data in YOLO format:
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dataset/
├── images/
│ ├── train/
│ └── val/
└── labels/
├── train/
└── val/
2. Create a Configuration File
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# data.yaml
train: ./dataset/images/train
val: ./dataset/images/val
nc: 5 # number of classes
names: ['scratch', 'crack', 'dent', 'corrosion', 'missing']
3. Train the Model
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model = YOLO('yolov8n.pt') # Load pretrained model
# Train the model
results = model.train(
data='data.yaml',
epochs=100,
imgsz=640,
batch=16,
device=0 # GPU device
)
Real-time Detection
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# Load trained model
model = YOLO('best.pt')
# Run detection on image
results = model('defect_image.jpg')
# Display results
for r in results:
im_array = r.plot()
cv2.imshow('Defect Detection', im_array)
cv2.waitKey(0)
Performance Optimization
Tips for Production Deployment
- Use TensorRT for faster inference
- Optimize image size based on your defect sizes
- Implement batching for multiple images
- Use confidence thresholds to reduce false positives
Results
On a typical PCB defect dataset:
- mAP@50: 94.5%
- Inference Time: 28ms per image
- False Positive Rate: < 2%
Conclusion
YOLO provides an excellent balance of speed and accuracy for defect detection applications. With transfer learning and proper dataset preparation, you can achieve production-ready results quickly.
Next Steps
- Fine-tune hyperparameters
- Experiment with different YOLO variants (YOLOv8s, YOLOv8m)
- Deploy to edge devices
- Implement active learning pipeline
Recommended Resources
Want to learn more? Check out these resources:
- Hands-On Machine Learning Book - Comprehensive ML guide including computer vision
- NVIDIA RTX GPU - Recommended for training YOLO models
- YOLOv8 Official Documentation - Complete guide and examples
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