The startup develops an automatic threat detection system for security X-ray screening, utilizing artificial intelligence and computer vision technology to achieve high detection accuracy and sub-second image throughput. This system integrates seamlessly with existing X-ray equipment, providing ongoing threat updates to enhance border control and reduce false alarm rates in the aviation industry.
Funding
$3.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Security X-ray screening relies heavily on human operators, leading to high failure rates and false alarms due to fatigue, distractions, and the increasing complexity of prohibited items. Slow throughput at security checkpoints also causes frustration and delays for passengers.
Solution
NeuralGuard's EyeFox is an AI-powered threat detection system designed to automate and enhance security screening processes. Utilizing deep learning and computer vision, EyeFox analyzes X-ray images in real-time to identify hidden threats with high accuracy. The system integrates with existing X-ray and CT equipment, providing operators with immediate threat detection and reducing the need for manual inspections. By minimizing false alarms and improving detection rates, EyeFox aims to increase throughput, lower operational costs, and create a safer, more secure environment.
Target Audience
The primary target audience includes aviation and mass transportation, law enforcement, event security, government buildings, e-commerce, maritime security, border control, critical infrastructure installations, mail screening, and cargo security.
Features
- AI-powered threat detection for X-ray and CT security screening
- Sub-second detection time for rapid analysis
- Algorithms trained on a vast dataset of over 3 million threat and non-threat images
- Continuously improving algorithms that adapt to new threats
- Minimizes false alarm rates to reduce manual inspections
- Maximizes throughput to decrease wait times
- Centralized image processing network that improves with data aggregation