Periodic Reporting for period 1 - SEAGUARD (Sea Environmental Awareness and Guard enhanced with Unmanned AI Robotic Detection)
Okres sprawozdawczy: 2024-10-01 do 2025-12-31
Strengthening surveillance along EU borders is essential to detect and prevent illegal activities, particularly through real-time maritime situational awareness. The SEAGUARD project aims to develop an integrated cross-border maritime surveillance system that combines multiple sensing technologies deployed on fixed and mobile platforms. These include unmanned vehicles (UxVs), fixed buoys, submarine cables, and advanced data analytics tools to monitor and identify threats across air, surface, and underwater domains. By delivering real-time intelligence to border authorities, the system will support timely and effective responses.
SEAGUARD’s main objectives are to:
1. Implement a holistic, autonomous robotic surveillance system capable of continuous operation.
2. Develop an AI-based system for multi-source threat detection and classification.
3. Increase system endurance to ensure higher operational availability and reliability.
4. Strengthen cross-border and cross-sectoral cooperation through improved information sharing among neighbouring EU Member States with shared external borders.
5. Demonstrate the system in representative operational scenarios.
6. Contribute to policy recommendations on the use of autonomous robotic systems in maritime surveillance
SEAGUARD’s main objectives are to:
1. Implement a holistic, autonomous robotic surveillance system capable of continuous operation.
2. Develop an AI-based system for multi-source threat detection and classification.
3. Increase system endurance to ensure higher operational availability and reliability.
4. Strengthen cross-border and cross-sectoral cooperation through improved information sharing among neighbouring EU Member States with shared external borders.
5. Demonstrate the system in representative operational scenarios.
6. Contribute to policy recommendations on the use of autonomous robotic systems in maritime surveillance
The activities focus on defining requirements, designing the system architecture, developing sensing capabilities, enabling UxV residency, and ensuring overall integration of SEAGUARD.
Work begins with specifying use cases, operational and technical requirements, and key performance indicators (KPIs) to benchmark SEAGUARD against traditional methods. Engagements with end users identify relevant procedures, practices, and bilateral agreements for cross-border and international missions.
At the architectural level, the general system design is defined, including the SEAGUARD Interoperability Platform to ensure data exchange among heterogeneous assets (e.g. UxVs). A resilient, multipath, multi-domain communication system is initially designed to enhance reliability, throughput, and interoperability, alongside requirements and implementation of the command and control interface (C2I) and asset mapping.
In sensing, relevant KPIs and high-performing sensors are identified and characterized. Detection algorithms are developed for RADAR, SONAR, passive acoustics, and optical imagery, along with classification algorithms for optical data, followed by performance evaluation.
For UxV residency, docking architectures are defined to enable minimal human intervention, including preliminary specifications for docking and landing capabilities.
Finally, integration activities include coordination with FRONTEX to explore mechanisms for information exchange, particularly through the Common Information Sharing Environment (CISE).
Work begins with specifying use cases, operational and technical requirements, and key performance indicators (KPIs) to benchmark SEAGUARD against traditional methods. Engagements with end users identify relevant procedures, practices, and bilateral agreements for cross-border and international missions.
At the architectural level, the general system design is defined, including the SEAGUARD Interoperability Platform to ensure data exchange among heterogeneous assets (e.g. UxVs). A resilient, multipath, multi-domain communication system is initially designed to enhance reliability, throughput, and interoperability, alongside requirements and implementation of the command and control interface (C2I) and asset mapping.
In sensing, relevant KPIs and high-performing sensors are identified and characterized. Detection algorithms are developed for RADAR, SONAR, passive acoustics, and optical imagery, along with classification algorithms for optical data, followed by performance evaluation.
For UxV residency, docking architectures are defined to enable minimal human intervention, including preliminary specifications for docking and landing capabilities.
Finally, integration activities include coordination with FRONTEX to explore mechanisms for information exchange, particularly through the Common Information Sharing Environment (CISE).
Evaluation of AI Tools in Maritime Environments (Objectives #1 & #2)
SEAGUARD evaluated and selected AI tools for maritime object detection, classification, and anomaly detection. Since most AI vision research targets terrestrial environments, WP5 focused on adapting and benchmarking methods for maritime use. Among more than 10 tested algorithms, Vision Transformers (ViT) showed the best object detection performance, while YOLO proved effective for classification and localization.
Acoustic vessel classification remains limited by small, heterogeneous datasets. To address this, a method was developed to calibrate and merge diverse hydrophone datasets into a unified corpus, including data augmentation with varied ambient noise. A cepstrum-based ranging technique was also created to estimate target distance from single hydrophones, enabling better fusion with AIS, radar, and camera systems.
Additional work included deploying YOLOv26 (January 2026), applying deep neural networks and Isolation Forest for anomaly detection, and performing detailed AIS trajectory analysis (e.g. Haversine-based calculations). A Composite Maritime Anomaly Detection Quality Index was introduced for AIS model evaluation. A flexible Streamlit-based dashboard was also developed to support stakeholders and enhance operational performance.
SEAGUARD Interoperability Framework (Objective #4)
WP7 finalized the SEAGUARD Interoperability Framework (S.IF) defining rules, protocols, and data formats that enable heterogeneous entities to exchange information and collaborate within a shared digital federation.
S.IF establishes a virtual interoperability space where services, systems, and applications from different authorities can integrate, share data, and build joint capabilities. Entities joining the federation must comply with S.IF standards, becoming S.IF nodes via connectors or gateways, allowing them both to contribute and access services and data.
The framework includes two main functionality sets: Core functionalities for managing the interoperability space, and AI functionalities for mission awareness and decision support. The resulting architecture is flexible, secure, and adaptable to diverse maritime security scenarios.
Docking (Objective #3)
Docking/landing of unmanned systems (WP9) is an active research topic, especially in maritime environments. Contributions to the scientific literature and to the technological landscape are expected. During this reporting period, a review of the state-of-the-art was performed to set the foundations for future developments. A systematic approach was presented, identifying all relevant building blocks that are common to UxVs, regardless of their domains. A preliminary architecture was developed and the main components were identified. These developments pave the way for potentially innovative solutions. A novel approach to UUV docking is under development.
SEAGUARD evaluated and selected AI tools for maritime object detection, classification, and anomaly detection. Since most AI vision research targets terrestrial environments, WP5 focused on adapting and benchmarking methods for maritime use. Among more than 10 tested algorithms, Vision Transformers (ViT) showed the best object detection performance, while YOLO proved effective for classification and localization.
Acoustic vessel classification remains limited by small, heterogeneous datasets. To address this, a method was developed to calibrate and merge diverse hydrophone datasets into a unified corpus, including data augmentation with varied ambient noise. A cepstrum-based ranging technique was also created to estimate target distance from single hydrophones, enabling better fusion with AIS, radar, and camera systems.
Additional work included deploying YOLOv26 (January 2026), applying deep neural networks and Isolation Forest for anomaly detection, and performing detailed AIS trajectory analysis (e.g. Haversine-based calculations). A Composite Maritime Anomaly Detection Quality Index was introduced for AIS model evaluation. A flexible Streamlit-based dashboard was also developed to support stakeholders and enhance operational performance.
SEAGUARD Interoperability Framework (Objective #4)
WP7 finalized the SEAGUARD Interoperability Framework (S.IF) defining rules, protocols, and data formats that enable heterogeneous entities to exchange information and collaborate within a shared digital federation.
S.IF establishes a virtual interoperability space where services, systems, and applications from different authorities can integrate, share data, and build joint capabilities. Entities joining the federation must comply with S.IF standards, becoming S.IF nodes via connectors or gateways, allowing them both to contribute and access services and data.
The framework includes two main functionality sets: Core functionalities for managing the interoperability space, and AI functionalities for mission awareness and decision support. The resulting architecture is flexible, secure, and adaptable to diverse maritime security scenarios.
Docking (Objective #3)
Docking/landing of unmanned systems (WP9) is an active research topic, especially in maritime environments. Contributions to the scientific literature and to the technological landscape are expected. During this reporting period, a review of the state-of-the-art was performed to set the foundations for future developments. A systematic approach was presented, identifying all relevant building blocks that are common to UxVs, regardless of their domains. A preliminary architecture was developed and the main components were identified. These developments pave the way for potentially innovative solutions. A novel approach to UUV docking is under development.