Ship Anomaly Detection
Recognition of Maritime Traffic Anomalies
Understanding maritime traffic, real-time anomaly alerts, and early collision risk detection.
Advanced maritime traffic intelligence platform: Monitoring ship movements and alerting on anomalous events to support decision-making through advanced analytics.
Scenario
Deep learning applied to the analysis of standard maritime traffic parameters:
Movement pattern analysis;
Statistical and deep anomaly detection;
Real-time behavioral pattern recognition;
Machine learning algorithms dynamically adjust thresholds based on historical and real-time data trends.
Solution
Shadow Fleet Monitoring:
Real-time data collection and AI-based analysis enable the detection of traffic discrepancies, assessment of collision risks, and monitoring of maritime activity near subsea cables and infrastructure, ensuring automated threat detection and prevention.
Real-Time Collision Prevention:
Automated collision risk assessment:
AI-driven Estimated Time of Arrival (ETA) calculation.
Results
- Improved maritime traffic anomaly detection across 3 anomaly types:
- A newly developed deep learning AI algorithm detects vessel trajectory patterns;
- Anomalies related to discrepancies in course and heading;
- Anomalies due to irregularities in speed changes.
- 35% reduced manual monitoring need: Automated monitoring and alerts reduce the need for manual actions.
- Incident resolution time reduced by 1 hour: The average time needed to resolve detected anomalies or security threats.
The system significantly improves maritime traffic safety and control.
