8. Computer Vision Reference Architecture - stanlypoc/AIRA GitHub Wiki

Computer Vision Reference Architecture

1. Introduction

1.1 Purpose

Standardized architecture patterns for implementing computer vision solutions across maturity levels (Basic → Advanced → Autonomous).

1.2 Audience

  • Data Scientists
  • ML Engineers
  • Solution Architects
  • Security/Compliance Teams

1.3 Scope & Applicability

In Scope:

  • Image classification
  • Object detection
  • Semantic segmentation
  • Model training/inference pipelines

Out of Scope:

  • Non-visual AI models
  • Hardware-specific optimizations

1.4 Assumptions & Constraints

Prerequisites:

  • Python 3.8+
  • Basic ML understanding

Technical Constraints:

  • GPU availability for training
  • Minimum 16GB RAM

Ethical Boundaries:

  • No facial recognition in public spaces
  • Bias mitigation required

1.6 Example Models

Type Examples
Basic ResNet, MobileNet
Advanced YOLOv8, Mask R-CNN
Autonomous CLIP, Segment Anything

2. Architectural Principles

🖼️ 2.1 Architecture Principles for Computer Vision

1. Modality-Specific Optimization

Architect for the unique demands of visual data, such as high dimensionality, spatial context, and resolution variation.

  • Optimize pipelines based on image size (e.g., HD vs. 4K), format (e.g., RGB, grayscale), and frequency (video vs. still).
  • Choose appropriate data formats: JPEG, PNG, DICOM, TIFF.
  • Preprocess using standardized transformations (e.g., resize, normalize, denoise).

2. Model Efficiency and Scalability

Prioritize efficient inference using scalable architectures.

  • Use optimized models for deployment: MobileNet, YOLO, EfficientNet, etc.
  • Apply pruning, quantization, or distillation for resource-limited environments.
  • Deploy via TensorRT, ONNX Runtime, or NVIDIA Triton for GPU acceleration.

3. Edge + Cloud Synergy

Enable intelligent partitioning of workloads across edge and cloud.

  • Perform inference at the edge for low-latency decisions (e.g., object detection on drones, cameras).
  • Delegate model training and retraining to cloud platforms.
  • Use lightweight models at the edge and fall back to cloud APIs when needed.

4. Pipeline Modularity

Structure CV systems as modular pipelines with reusable components.

  • Use stages like: ingestion → preprocessing → inference → postprocessing → visualization.
  • Support interchangeable models (e.g., switching from ResNet to ViT).
  • Containerize components using Docker and deploy via Kubernetes or serverless functions.

5. Temporal Awareness (for Video)

Incorporate temporal context in video processing.

  • Use spatiotemporal models (e.g., SlowFast, 3D CNN, I3D) for action recognition or tracking.
  • Buffer frames intelligently for real-time inference without excessive memory usage.
  • Apply temporal smoothing or ensemble logic to stabilize predictions.

6. Privacy and Security

Handle sensitive visual data with privacy-first design.

  • Apply face blurring, license plate masking, or DICOM redaction where applicable.
  • Encrypt visual data in storage and transit (TLS 1.3, KMS).
  • Monitor access logs and enforce RBAC on labeled datasets and model endpoints.

7. Explainability and Debugging

Provide interpretable outputs for visual decision-making systems.

  • Use Grad-CAM, LIME, or attention visualizations to show what the model “saw.”
  • Log intermediate tensors or heatmaps for offline review.
  • Create visual dashboards with overlays for model insights (bounding boxes, segmentation masks).

8. Labeling, Feedback & Retraining

Integrate active learning loops for continuous model improvement.

  • Enable human-in-the-loop correction workflows for model outputs.
  • Use auto-labeling with confidence thresholds to bootstrap new datasets.
  • Version datasets and retrain models using CI/CD pipelines (e.g., Kubeflow, SageMaker Pipelines).

9. Multimodal Fusion (When Applicable)

Support integration with other modalities like audio, text, or sensor data.

  • Fuse visual embeddings with metadata (e.g., location, timestamp).
  • Enable cross-modal alignment for use cases like OCR + NLP, lip-reading, or video captioning.

10. Fail-Safe & Redundancy Design

Build for graceful degradation and fallbacks.

  • If camera feed drops or inference fails, trigger alerts or revert to last known good prediction.
  • Maintain fallback logic for critical systems (e.g., autonomous vehicles, surveillance).
  • Use image quality checks to filter corrupted or low-confidence frames.

11. Performance Observability

Monitor system health with vision-specific metrics.

  • Use real-time dashboards to track frame rate, model confidence, latency, and inference success rates.
  • Collect metrics like IoU, mAP, precision, recall over time.
  • Integrate with Prometheus, Grafana, MLflow, or custom telemetry platforms.

12. Compliance & Ethical Use

Align with legal and ethical guidelines for AI in vision systems.

  • Avoid unauthorized use of facial recognition or biometric tracking.
  • Clearly document data sources, consent, and purpose.
  • Adopt model cards and data datasheets to maintain transparency.

2.2 Standards Compliance

  1. Security & Privacy

    • Must comply with: GDPR Article 22, ISO/IEC 27001
    • Practical tip: Implement data masking in previews

  2. Ethical AI

    • Key standards: IEEE 7000-2021
    • Checklist item: Bias assessment report

2.3 Operational Mandates

5 Golden Rules:

  1. Never store raw biometric data
  2. Model cards must accompany deployments
  3. Minimum 95% test coverage
  4. Real-time monitoring for drift
  5. Human-in-the-loop for critical decisions

Sample Audit Log:

{
  "timestamp": "2023-11-20T14:23:12Z",
  "model_id": "cv-prod-003",
  "input_hash": "a1b2c3...",
  "prediction": {"class": "defect", "confidence": 0.87},
  "anomaly_flag": false
}

3. Architecture by Technology Level

3.1 Level 2 (Basic)

Definition:
Pre-trained models with fine-tuning for specific tasks.

Key Traits:

  • Batch processing
  • Accuracy <90%
  • Single modality

Logical Architecture:

graph LR
    A[Image Source] --> B[Preprocessor]
    B --> C[ResNet50]
    C --> D[Prediction]
    D --> E[Results Storage]

Cloud Implementations:

Provider Services
Azure Azure ML + Blob Storage
AWS SageMaker + S3
GCP Vertex AI + Cloud Storage

Deployment:

  • Infrastructure: 1 GPU node
  • Scalability: Manual scaling
  • Security: IAM + Storage encryption

3.2 Level 3 (Advanced)

Definition:
Custom architectures with multi-model pipelines.

Key Traits:

  • Real-time processing
  • Accuracy ≥92%
  • Multi-modal inputs

Logical Architecture:

graph LR
    A[Camera Stream] --> B[Preprocessor]
    B --> C[YOLOv8 Detector]
    C --> D[Tracker]
    D --> E[Postprocessor]
    E --> F[API Output]

Cloud Implementations:

Provider Services Stack Specialized Components
Azure - Azure ML Pipelines- Kubernetes Service- Cosmos DB- Application Insights NVIDIA Triton on A100 VMs
AWS - SageMaker Pipelines- EKS- DynamoDB- CloudWatch Inferentia Chips for optimization
GCP - Vertex AI Workbench- GKE- Firestore- Operations Suite TPU v4 Pods
Open-Source - Kubeflow- Redis- Prometheus Seldon Core for model serving

Cross-Cutting Concerns:

Area Implementation
Performance Triton Inference Server
Observability Prometheus + Grafana
CI/CD MLflow + GitHub Actions

3.3 Level 4 (Autonomous)

Definition:
Self-improving systems with explainability.

Key Traits:

  • Continuous learning
  • Accuracy ≥95%
  • Causal reasoning

Logical Architecture:

graph LR
    A[Edge Devices] --> B[Federated Learning]
    B --> C[AutoML Optimizer]
    C --> D[Explainability Layer]
    D --> E[Self-Healing System]

Cloud Implementations:

Provider Autonomous Stack Key Differentiators
Azure - Azure Autonomous ML- Confidential Computing- Blockchain Ledger- Digital Twins Private 5G Edge Integration
AWS - SageMaker Autopilot- IoT Greengrass- QLDB- RoboMaker Bedrock Foundation Models
GCP - Vertex AI AutoML Vision- Anthos- BigQuery ML- Automotive AI Gemini Multimodal Integration
Open-Source - Ray Federated Learning- Feast Feature Store- BentoML- OpenMined Homomorphic Encryption Support

Governance:

  • Versioning: Model Registry
  • Decision Log: Blockchain-based

4. Glossary & References

Term Definition
IoU Intersection over Union metric
Data Augmentation Synthetic training data generation

References:

  1. MLPerf Benchmark
  2. ONNX Runtime Documentation