Detection Model Fundamentals
Understanding how AI detection models work helps you configure them effectively for your specific requirements.
How Detection Works
AI models analyze video frames to identify objects, people, and behaviors. Pre-trained models recognize common objects like vehicles, people, and equipment. Confidence scores indicate certainty of detections. Filtering by confidence threshold balances sensitivity vs. false positives.
Available Model Types
Object detection identifies and locates items in video frames. Classification models categorize detected objects into types. Behavior analysis recognizes activities and actions. Anomaly detection flags unusual patterns without explicit training.
Model Selection
Choose models appropriate for your detection requirements. General-purpose models work for common objects and scenarios. Specialized models provide higher accuracy for specific domains. Consider processing requirements when selecting model complexity.
Configuring Object Detection
Object detection identifies and tracks items of interest in your video feeds.
Detection Zone Setup
Define regions of interest to focus analysis and reduce false positives. Draw polygons around areas requiring monitoring. Exclude zones with irrelevant activity. Multiple zones can have different detection settings.
Object Classes
Select object types to detect from available classes. Common classes include person, vehicle, truck, bicycle, and animal. Enable only relevant classes to optimize performance. Fine-tune detection for each class independently.
Confidence Thresholds
Set minimum confidence for detection acceptance. Higher thresholds reduce false positives but may miss valid detections. Start with 70% confidence and adjust based on results. Different classes may require different thresholds.
Safety Compliance Monitoring
AI detection enables automated monitoring of safety requirements and compliance.
PPE Detection
Configure detection of personal protective equipment including hard hats, safety vests, and safety glasses. Set compliance requirements per zone (e.g., hard hat required in construction area). Generate alerts for non-compliance with configurable severity levels.
Restricted Area Monitoring
Define zones where presence triggers alerts. Configure authorized vs. unauthorized personnel based on detected attributes. Set time-based rules (e.g., restricted after hours). Integrate with access control for validated authorization.
Behavior Monitoring
Detect unsafe behaviors such as running in restricted areas. Monitor for loitering or unusual movement patterns. Identify crowd formation and density violations. Configure alert thresholds based on risk assessment.
Custom Model Training
Train custom models to detect objects or behaviors specific to your operations.
Data Collection
Gather representative images of objects to detect. Include variations in lighting, angle, and occlusion. Minimum several hundred images recommended per class. Balance dataset across different conditions and scenarios.
Annotation
Label images with bounding boxes around objects of interest. Use consistent annotation guidelines for accuracy. Review annotations for quality before training. Consider professional annotation services for large datasets.
Model Training
Upload annotated dataset to training interface. Configure training parameters (epochs, learning rate). Monitor training progress and validation metrics. Evaluate trained model performance before deployment.
Deployment
Deploy trained model to production environment. Test with live video feeds in limited scope first. Monitor detection accuracy and adjust thresholds. Iterate on training data to improve performance.
Alert Configuration
Configure alerts to notify appropriate personnel when detections occur.
Alert Rules
Create rules combining detection events with conditions. Example: Alert when person detected in restricted zone after 6pm. Configure alert severity based on risk level. Enable alert suppression to prevent notification flooding.
Notification Channels
Configure email, SMS, or app notifications per alert type. Include detection snapshots for context. Route alerts to appropriate response teams. Enable escalation for unacknowledged alerts.
Integration
Connect detection alerts to security management systems. Trigger automated responses such as alarm activation. Log all detections for analysis and compliance. Export detection data to business intelligence tools.
Performance Optimization
Optimize model performance to balance accuracy, speed, and resource utilization.
Processing Efficiency
Adjust frame rate analysis based on requirements. Skip frames for slowly changing scenes. Use motion-triggered analysis to reduce processing load. Balance resolution with processing speed.
Accuracy Improvement
Review false positives and negatives regularly. Adjust confidence thresholds based on error analysis. Refine detection zones to exclude problem areas. Consider environmental factors affecting accuracy.
Model Updates
Periodically retrain models with new data. Update when operational conditions change. Test updated models before production deployment. Maintain version history for rollback capability.
