Quality Inspection Problem
In industrial manufacturing, traditional quality inspection relies on human eyes — slow, inconsistent, and expensive labor. Computer Vision solves this by automatically detecting product defects with high speed and accuracy.
Why Choose YOLOv8?
YOLOv8 from Ultralytics is state-of-the-art object detection with advantages for industrial applications:
- Speed: 30-60 FPS on mid-range GPU (RTX 3060), sufficient for high-speed lines
- Accuracy: mAP50-95 superior to earlier YOLO versions
- Easy training: Simple Python API, transfer learning from pre-trained
- Multi-format export: Export to ONNX, TensorRT, OpenVINO for inference optimization
Image Processing Pipeline
1. Data Collection
Use industrial cameras (Basler, FLIR) with GigE Vision or USB3 Vision. Uniform lighting is critical — recommend LED ring lights or backlights depending on product type.
2. Data Labeling
Use Roboflow or CVAT to label defect types: scratches, solder defects, deformations. Need minimum 500-1000 images per class for stable model performance.
3. Model Training
from ultralytics import YOLO
model = YOLO('yolov8n.pt') # nano model for edge device
results = model.train(
data='defect_dataset.yaml',
epochs=100,
imgsz=640,
batch=16
)
4. Production Deployment
Inference pipeline runs on edge PC (Jetson Orin or Edge AI device) placed next to production line. Results sent to SCADA system via MQTT or OPC UA for automatic defect rejection.
Real-World Results
At electronics factory in Bac Ninh, VnRobo's CV system achieved:
- Defect detection accuracy: 98.5%
- Processing speed: 45 FPS (22ms/frame)
- 80% reduction in manual inspection labor
- ROI: payback after 6 months deployment
Challenges and Solutions
Biggest challenge is domain shift — when lighting or product changes, model needs retraining. Solution is building CI/CD pipeline for ML: auto-collect new images, retrain and deploy with Docker without stopping production line.
Also, combining computer vision with digital twin allows simulating and optimizing inspection system before real deployment, significantly reducing testing cost.
