Computer Vision
AI-powered image and video processing solutions for industrial and commercial applications.

Computer Vision Solutions
We develop computer vision systems that extract actionable insights from visual data. From real-time video analytics to complex image understanding, we build solutions that see and understand.
Beyond Detection
Vision Pipeline
flowchart LR
subgraph Input["Data Acquisition"]
A[Camera/Sensor] --> B[Frame Capture]
end
subgraph Processing["Processing Pipeline"]
B --> C[Preprocessing]
C --> D[Model Inference]
D --> E[Post-processing]
end
subgraph Output["Results"]
E --> F[Detections]
E --> G[Tracking]
E --> H[Analytics]
end
subgraph Action["Response"]
F & G & H --> I[Alerts]
F & G & H --> J[Control]
F & G & H --> K[Reports]
end
style Input fill:#e0f2fe,stroke:#0284c7
style Processing fill:#fef3c7,stroke:#d97706
style Output fill:#dcfce7,stroke:#16a34a
style Action fill:#fce7f3,stroke:#db2777
Real-Time Video Analytics
| Application | Capabilities | Industries |
|---|---|---|
| Object Detection | People, vehicles, products, custom objects | Retail, security, logistics |
| Activity Recognition | Pose estimation, action classification | Healthcare, sports, safety |
| Anomaly Detection | Unusual patterns, events | Security, manufacturing |
| Crowd Analysis | Counting, density, flow | Events, retail, transport |
Industrial Vision
Quality at Scale
| Application | Description | Accuracy Target |
|---|---|---|
| Defect Detection | Surface inspection, dimensional verification | >99.5% |
| OCR & Documents | Text extraction, form processing | >99% |
| Barcode/QR | High-speed scanning solutions | >99.9% |
| Assembly Verification | Component placement validation | >99% |
Technology Stack
| Category | Technologies | Purpose |
|---|---|---|
| Frameworks | OpenCV, PyTorch, TensorFlow | Development and training |
| Models | YOLO, EfficientDet, Detectron2 | Detection and segmentation |
| Deployment | TensorRT, ONNX Runtime, OpenVINO | Production inference |
| Edge Hardware | Jetson, Coral TPU, Intel NCS | On-device processing |
Model Selection Guide
| Model Family | Speed | Accuracy | Best For |
|---|---|---|---|
| YOLOv8 | Fast | High | Real-time detection |
| EfficientDet | Medium | Very High | Balanced applications |
| Detectron2 | Slower | Highest | Complex segmentation |
| MobileNet | Very Fast | Good | Edge deployment |
AR/VR/XR Development
Extended reality solutions combining computer vision with immersive experiences:
| Solution | Description | Applications |
|---|---|---|
| Spatial Computing | 3D environment understanding | Navigation, mapping |
| Mixed Reality Training | Interactive learning environments | Industrial training |
| Virtual Showrooms | Product visualization | Retail, automotive |
| AR Maintenance | Guided repair procedures | Field service |
Implementation Process
- Data Assessment: Evaluate existing imagery and define requirements
- Model Selection: Choose architecture based on accuracy/speed trade-offs
- Training & Optimization: Custom training with your data
- Edge Deployment: Optimize for target hardware platform
- Integration: Connect to existing systems and workflows
- Monitoring: Track accuracy metrics and model drift
Have a vision project? Let’s discuss how we can help.
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