MineDL delivers a next-generation AI solution for real-time detection and classification of moored, drifting, bottom, and influence mines in support of naval operations. Traditional mine countermeasure approaches struggle with limited detection range and poor adaptability in complex, variable undersea environments. MineDL overcomes these limitations with advanced machine learning algorithms that perform reliably even with sparse data inputs—capable of identifying both known and novel mine threats.

Designed for seamless integration with existing naval platforms—including AN/AQS-20, Mk18 Mod 2 UUV, AQS-24, and Barracuda—MineDL extends detection coverage and enables intelligent, adaptive tracking across dynamic mission environments.

By enhancing threat recognition, mission efficiency, and situational awareness, MineDL significantly strengthens maritime safety and operational readiness in contested and unpredictable waters.

MsObjDET is a next-generation, AI-enhanced system engineered for real-time detection and interpretation of multi-spectral sensor data in operationally complex environments. Traditional systems often fail to reconcile data inconsistencies across radar, EO/IR, and off-board sources—resulting in delayed, unreliable object recognition and target tracking.

Harnessing advanced AI strategies, MsObjDET delivers rapid, adaptive analysis by intelligently integrating inputs from diverse sensor modalities. It ensures stable target identification and accurate situational understanding under dynamic motion, signal degradation, or multi-observer perspectives.

From naval combat systems and airborne ISR to autonomous surveillance networks, MsObjDET transforms how spectral data is utilized for decision-making. By providing real-time, cross-domain object detection with high confidence, it enhances mission performance and survivability across both military and defense-driven environments.

The U.S. Army Materiel Command (AMC) and other DoD agencies define demilitarization (DEMIL) as the process of eliminating the functional and military characteristics of equipment to prevent its reuse in any military capacity. During this process, obsolete munitions are dismantled and incinerated, producing metal scraps. While incineration aims to destroy all energetic materials, some residues may remain due to the hollow or vented structures of certain scraps. Identifying and eliminating these remaining hazards is critical before transferring the materials for commercial recycling.

Currently, the Army relies on manual inspections by two certified personnel to determine whether a metal scrap is safe (MDAS) or potentially hazardous (MPPEH). However, this approach is limited by its reliance on human labor, inconsistent accuracy, judgment variability, and low operational efficiency.

To address these challenges, MetalScrap offers an automated, image-based inspection platform that enhances accuracy, safety, and processing speed. By analyzing advanced X-ray imagery, the system rapidly identifies potential energetic residues and classifies metal scraps accordingly—streamlining the inspection process and reducing reliance on human judgment.

MetalScrap also generates risk assessments based on material condition and relevant safety standards, offering valuable support for operational decision-making. Its user-friendly interface enables inspectors to review and manage inspections with ease, while significantly lowering labor costs.

Designed for seamless adoption across key Army organizations—including JMC, AMC, ARDEC, AMCOM, and the CCDC Armaments Center—MetalScrap modernizes the DEMIL process and holds strong potential for broader use in defense and commercial metal recycling operations.

Atom Trap Trace Analysis (ATTA) is a breakthrough technology in rare gas isotope detection, offering unmatched selectivity and sensitivity for single-atom measurements. Its ability to accurately detect trace radioisotopes plays a vital role in nuclear monitoring, enabling agencies such as the Defense Threat Reduction Agency (DTRA) to rapidly assess nuclear fuel processing, weapons testing, and accidental leaks—both underground and above ground.

Current Limitations

The current approach used by DTRA relies on conventional statistical methods applied to selected regions of ATTA imaging data. However, this process presents several challenges

• Limited accuracy due to signal interference
• High uncertainty during quantification
• Inability to effectively process extremely low- or high-abundance samples
• Slow turnaround times and reduced operational efficiency

A²TTA Solution

To overcome these challenges, InfoBeyond has developed A²TTA, a next-generation solution for real-time isotope detection and quantification. By replacing manual interpretation with intelligent image-based analysis, A²TTA enhances speed, accuracy, and reliability under a wide range of conditions.

Key Advantages of A²TTA

• High Accuracy in Noisy Conditions: Maintains precision even in low signal-to-noise environments
• Fully Automated Operation: Supports remote deployment through a compact and portable platform
• Efficient and Scalable: Enables faster, more cost-effective analysis for broader operational use

Applications of A²TTA

A²TTA significantly expands the utility of trace isotope analysis across multiple sectors:

• Radiometric age dating
• Medical imaging and diagnostics
• Industrial system monitoring
• Long-term nuclear safety assessment
• Space science and particle physics

In short, while traditional A²TTA systems face high costs and processing constraints, A2TTA lowers barriers to adoption—opening new possibilities for ultra-sensitive isotope detection across scientific, industrial, and national security domains.