Anomaly Detection & QC
AI-Powered Anomaly Detection & Quality Control
Real-time defect detection and quality control with AI technology
detection accuracy
response time
waste reduction
Anomaly Detection Methods
Visual Inspection
Computer Vision
- • CNN-based Detection
- • Object Detection (YOLO, R-CNN)
- • Semantic Segmentation
- • Edge Detection
Deep Learning
- • Autoencoders
- • VAE (Variational)
- • GAN-based Detection
- • Siamese Networks
Statistical Methods
Classical Algorithms
- • Z-Score Analysis
- • Isolation Forest
- • One-Class SVM
- • Local Outlier Factor
Time Series
- • ARIMA Models
- • Seasonal Decomposition
- • Prophet Forecasting
- • LSTM Networks
IoT Sensor Analysis
Sensor Fusion
- • Multi-modal Data
- • Kalman Filtering
- • Particle Filters
- • Bayesian Networks
Real-time Processing
- • Stream Processing
- • Edge Computing
- • Apache Kafka
- • Redis Streams
Quality Control Applications
Manufacturing Industry
Surface Defect Detection
Detect scratches, cracks, and surface damage
Dimensional Inspection
Verify dimensions and shapes against standards
Assembly Verification
Verify assembly completeness and correctness
Food & Pharmaceutical
Contamination Detection
Detect foreign objects and contamination
Package Integrity
Verify packaging integrity and sealing
Batch Quality Control
Monitor quality across production batches
Technology Stack
Machine Learning
- TensorFlow
- PyTorch
- Scikit-learn
- XGBoost
Computer Vision
- OpenCV
- YOLO
- Detectron2
- MMDetection
Processing
- Apache Spark
- Kafka Streams
- Redis
- NVIDIA TensorRT
Hardware
- Industrial Cameras
- GPU Computing
- Edge Devices
- IoT Sensors
Ready to Improve Production Quality?
Consult our anomaly detection and quality control experts