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) technique is capable of providing high sensitivity, single/multiple atom detection capabilities, and superb selectivity, which 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. Specifically, nuclear monitoring in real-time or near-real-time fashion requires ultra-fast processing time. Further, the isotope detection should be accomplished accurately in various contexts, e.g., low/high signal-to-noise ratio (S/N) images, spurious events, vibrations, cosmic radiations, operated in the ocean floater/unmanned aerial vehicle (UAV)/orbital vehicles.

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.

To address these challenges, A²TTA takes advantage of charge-coupled device (CCD)/electron-multiplying CCD (EMCCD) imaging technology and advanced deep learning methods to offer accurate inference capability for isotope identification and quantification in a real-time fashion. Particularly, A²TTA develops AI-based architectures, e.g., You Only Look Once (YOLO), advanced Transformer, etc., to provide accurate atomic identification and atom/isotope quantification under various complicated contexts and operational environments:

• A²TTA uses the leading YOLO algorithm and effectively learns a spectrum of image features to precisely identify the atom’s presence via classification, which can learn the atomic presence in a full range of count rates.
• A²TTA performs deep learning to deliver the atom number with the use of all imaging and its statistical features, which can quantify the atoms over the full range of abundance levels.
• A²TTA implements the state-of-the-art YOLO-based algorithm with improved optimization technology, which boosts the time efficiency to achieve real-time performance.