The United States (U.S.) Army Material Command (AMC) and other DoD agencies define demilitarization (DEMIL) as the destruction of the functional capabilities and inherent military features from DoD materials to prevent further usage of the equipment and materials from the originally intended military design or capabilities. During the DEMIL process, all obsolete munitions are dismantled and incinerated together to form metal scraps. Incineration destroys the energetics in the metal scraps. It is not unusual for some metal scraps to contain energetics even after incineration due to their hollowness and venting cavities. It is, therefore, important to identify and destroy this energetics before handling the metal scraps to commercial dealers for recycling. The common practice employed by the U.S. Army to ascertain the destruction of energetics in metal scraps is to inspect each metal scrap using two independent trained and certified inspectors. The inspectors classify the metal scraps as Material Documented as Safe (MDAS) or Material Potentially Possessing Explosive Hazard (MPPEH). Human based approaches have several limitations:

• Lack of automation due to human operations,
• Poor classification accuracy due to limited visual capability,
• Human judgment bias based on inspectors, and
• Low DEMIL time/cost-efficiency.

For addressing these challenges, MetalScrap takes advantage of X-ray technology, digital imaging, and advanced deep learning algorithms to provide an alternative metal scrap inspection method that is accurate, safe, and time-effective. Particularly, MetalScrap develops an AI (e.g., advanced CNN, Swin Transformer, YOLO)-based architecture to provide metal scrap inspection, classification, energetic residue identification, and flexible GUI (graphic user interfaces)-based human control:

• MetalScrap uses multiview-multimodal images (e.g., Transmission and/or Backscatter X-ray) as a training dataset to precisely identify explosives in images of metal scraps, and accurately classify such metal scraps as MPPEH in a matter of seconds. This removes the limitations of poor accuracy, human judgment bias, and safety risks present with a human inspection.
• MetalScrap utilizes a leading You Only Look Once (YOLO) algorithm to analyze the energetic types, location, and quantity to form a severity report for decision-making. MetalScrap-YOLO consists of multiple functions that provide segmentation and severity analysis based on adaptable material handling, safety and inspection standards (e.g., DODI 4140.62, DODM 4140.72), which is important for DoD applications.

As a software platform, UI (User Interface) allows inspectors to review, control, and manage the entire metallic scrap inspection process. MetalScrap transition can be used for Army JMC, AMC, ARDEC, AMCOM, and Army CCDC Armaments Center for automating and enhancing DEMIL process. It classifies metal scraps as MPPEH in a matter of seconds to facilitate AMC & JMC metal inspection automatically without the need to label dataset which reduces AMC & JMC metal scrap inspection labor cost. It also has a large market for customers with DoD-like requirements, e.g., metal scrap inspection for metal recycling.

Atom Trap Trace Analysis (ATTA) represents a revolutionary technology in rare gas isotope analysis, with exceptional selectivity and sensitivity for single atom detection. The criticality of accurate and efficient radioisotope detection in ATTA systems is particularly important for nuclear monitoring. This allows the Defense Threat Reduction Agency (DTRA) and other DoD agencies to rapidly monitor nuclear activities, including nuclear fuel processing/recycling, underground or on-land nuclear weapon tests, accidental nuclear leaks, etc.

Current Limitations

The current practice employed by DTRA is to apply a traditional numerical integration algorithm to the selected Region of Interest (ROI) of ATTA’s atomic fluorescence image, and derive the atom quantities via statistical fitting. The current approaches have several limitations:

• Low detection/quantification accuracies due to the spurious photon counts
• Introduces additional uncertainties during the quantification procedure
• Cannot handle ultra-low/high abundance samples
• Low efficiency due to the prolonged processing/analysis turnaround times

A²TTA Solution

To address these challenges, InfoBeyond advocates A²TTA (Real-time Isotope Identification and Quantification using YOLO-based Neural Network for Advanced Atom Trap Trace Analysis), a state-of-the-art solution designed to revolutionize real-time isotope identification and quantification. A2TTA harnesses the power of advanced You Only Look Once (YOLO)-based deep learning architecture.

Advantages of A²TTA

• High Precision and Robustness against Low S/N: Leverages YOLO-based A²TTA image learning for analytical precision even with low S/N ratio images.
• Analysis Automation: Utilizes a rugged, lightweight, and portable server for easy remote access.
• Real-time, Efficient, Cost-saving Isotope Analysis: Time-efficient and cost-effective solution enabling large-scale deployment.

Applications of A²TTA

A²TTA has emerged as a powerful technique for detecting trace radioisotopes of noble gases. Here's where it can be applied:

• Radiometric Dating
• Medical Diagnosis, Dose Optimization, and Medical Isotopes
• Industrial Apparatus Health Diagnosis
• Long-term Evaluation of Nuclear Safety
• Solar Neutrinos and Cosmic Physics

In summary, the use of A²TTA in ultrasensitive trace-isotope analysis is constrained by its high cost. A²TTA provides crucial values to lower the analyzing cost such that ATTA can significantly expand the application of ultrasensitive trace-isotope analysis in different industries.

Under the support of naval operations, MineDL provides a reliable AI-based solution for real-time detection and classification of moored, drifting, bottom, and influence mines. Traditional methods are limited in detection range and adaptability, particularly in diverse and complex underwater environments. MineDL addresses these challenges through sophisticated learning algorithms that require minimal data input, excelling at identifying both known and emerging mine threats.

MineDL integrates seamlessly with existing naval systems such as AN/AQS-20, Mk18 FOS, AQS-24, and Barracuda, providing extended detection coverage and dynamic tracking capabilities. This AI solution ensures enhanced operational safety and readiness, even in the most challenging maritime conditions.

AI-Fire offers a state-of-the-art AI-based solution for the real-time detection and analysis of fire ignition events triggered by collision impacts. Traditional analysis methods face significant challenges in accurately capturing the complex interactions between penetrating fragments, incendiary materials, and leaking fuels, often resulting in incomplete assessments.

Utilizing advanced deep learning algorithms, AI-Fire automates the detection and analysis of these ignition events, significantly enhancing safety and operational efficiency. The technology excels in monitoring spatiotemporal dynamics, providing detailed analyses that inform safety protocols and optimize fire suppression strategies.

AI-Fire's applications span across various domains, including defense, industrial safety, and forensic investigations, revolutionizing how fire hazards are managed and improving overall situational awareness. By offering precise, real-time insights, AI-Fire transforms fire ignition analysis, ensuring better safety and survivability in both military and civilian contexts.