InfoBeyond Technology is an innovative company specializing in AI, Computer Vision, Communications, and Cybersecurity within the Information Technology industry.

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320 Whittington PKWY, STE 303
Louisville, KY, USA 40222-4917
[email protected]
(502) 919 7050

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Our Strength

Computer Vision and AI

Instead of traditional machine learning, AI using deep learning finds the rich internal representations of features required for difficult tasks such as recognizing objects of interest or understanding the inherited properties of the object.

Our Solutions (1/3)

Object Classification, Detection, Recognition

MetalScrap: A Multimodal and Attention-Based AI (Advanced CNN, Swin Transformer, and YOLO) for Automated and Accurate Metallic Scrap Inspection

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.

Our Solutions (2/3)

Visual Tracking, Localization, and Analytics

A2TTA: Real-time Isotope Identification and Quantification using YOLO-based Neural Network for Advanced Atom Trap Trace Analysis

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.

AI-Based Parcel/Package Recognition and Identification via Advanced Computer Vision

Instead of human parcel sorting, the company is developing an automated parcel sorting systems in order to save labor costs. In such a system, a robot is designed to pick one parcel from a parcel pool and place it to a console connected to a conveyor belt. As tested, one robot can work as fast as several persons in a manner of 24/7/365.

Multi-pick especially is an issue that a robot picks two or more parcels/packages at the same time, resulting in multiple parcels are placed in a conveyor slot. Human has to manually re-sort with high attention on the conveyor belt. Therefore, it is essential to prevent multi-pick in various parcel or package processes.

As a response, an AI-based vision system is advocated to address the multi-pick challenges. The AI system improves the performance from both pre-pick and post-pick aspect. In the pre-pick process, AI algorithm based on a novel salient parcel detection algorithm is proposed using RGB and multispectral images to detect and localize those salient parcels in the pool. In the post pick process, it designs an innovative video-based detection method to detect if multiple parcels are picked. Instead of a single image, it uses a short video to learn multi-pick that is caused by the potential occlusion between parcels. The system targets a goal to meet a minimum performance of 0.01% multi-pick probability.

Our Solutions (3/3)

Image Reconstruction

Image/Signal Recovery or Reconstruction using Near-Optimal Matrix Completion Optimization under Noise

The image/signals captured from a device are often contaminated as these observed entries are collected in a noisy environment. Meanwhile, the image/signals could be unexpectedly corrupted for any reasons. Further, the image/signals may be down sampled (e.g., average downsampling, bicubic downsampling, or subsampling) with a given probability. These images/signals should be recovered with as little as loss of image/signal detail and precision. This process is called image/signal reconstruction. Given a highly incomplete image or signal dataset with noises, traditional approaches such as regression and statistics are very limited to exactly recover the missing information entries in the image or signaling dataset, especially when these data are dominated by unknown entries (e.g., 50% or more).

Under the Navy’s support, an image/signaling recovery or reconstruction method is developed resorting to Near-Optimal Matrix Completion (NOMC) technology. Especially, NOMC especially is a reliable matrix completion method that a low-rank image or signaling matrix could be precisely recovered with a high probability from a very low number of non-zero entries. Therefore, image/signal recovery becomes a matrix completion optimization problem with the consideration of noises. It addresses the limitation of current approach in processing the noisy matrix where the known entries are sampled in a noisy environment or they are contaminated by a number of environmental factors. In practical engineering applications, observation noises are everywhere and it is critical to “denoise” the negative impacts in the matrix completion algorithms. For a given low-rank data matrix, NOMC is able to recover the original matrix with the average Frobenius norm error of 10% while 80% of data entries are unknown. It can effectively reconstruct the original image with high quality from the downsampled image even if 50% image pixels are randomly removed.