Security X-ray machines powered by neural network AI: What You Need to Know

X-ray images are commonly used in security screening to identify and detect prohibited or illegal materials such as explosives, weapons, or drugs. However, manual inspection of these images is time-consuming, error-prone, and dependent on human factors. Therefore, there is a need for automated systems that can accurately and efficiently identify threats in X-ray images.

Integrating Artificial Intelligence (AI) in security infrastructure has become imperative for safeguarding public spaces and critical facilities in an era of technological advancements. Specifically, deploying AI-driven threat detection mechanisms in X-ray machines at security checkpoints marks a pivotal leap forward in enhancing our ability to identify and preempt potential risks. Deep Learning algorithms, sophisticated computational models inspired by the human brain’s neural networks, are at the forefront of this innovation.

Deep Learning, particularly convolutional neural networks (CNNs), is widely used for threat detection in X-ray images because of its ability to learn from large datasets and automatically extract features relevant to threat identification. Threat detection in X-ray images is critical for preserving public safety, as it can help identify dangerous or prohibited objects in luggage or vehicles. However, X-ray images pose many challenges for traditional image analysis methods, such as limited colour range, lower contrast, poor texture, and object overlap. CNNs can overcome these challenges by learning hierarchical representations of the images that capture both low-level and high-level features. This can improve the accuracy and efficiency of threat detection and reduce the human workload and error rate.

The benefits of using deep learning for automated threat detection in X-ray images are:

  • Reducing the workload and fatigue of human screeners by providing them with automated assistance.
  • Increasing security and safety by detecting threats that human screeners may miss.
  • Enhance the customer experience by reducing waiting time and false alarms at security checkpoints.

Neural Guard’s EyeFox leverages neural networks and deep learning to enhance security while maintaining operational speed in X-ray security screening. Neural Guard’s EyeFox utilises neural networks in its algorithm for security screening. Below are some of its benefits.

Deep Learning and Computer Vision:

  • EyeFox is a revolutionary AI-based system designed for automated threat detection in X-ray security screening.
  • It employs deep learning computer vision techniques, particularly convolutional neural networks (CNNs), to analyse X-ray images.
  • EyeFox creates a centralised image processing network.
  • As it processes more data, its value increases exponentially.
  • Sub-second detection time: EyeFox operates swiftly, enhancing security screening efficiency.
  • Lower operational cost: Efficient automated threat detection minimises manual inspections.

EyeFox revolutionises security screening by combining AI-driven threat detection with X-ray technology, improving safety without compromising operational efficiency.