How AI Is Making Metal Detection More Important Than Ever

Every day, millions of people pass through walk-through metal detectors at airports, schools, concert venues, courthouses, and office buildings. A vast majority barely think twice about this process or the fact that these devices serve as an essential firewall for their very own wellbeing. When a detector beeps, it’s typically seen as a minor annoyance or delay – once the cause is resolved, everyone continues on with their day.

It’s no secret that we’re living in a world faced with rising security tensions, growing crowds, and the proliferation of unfortunate incidents. The unsurprising ripple effect  of this is that the security industry is being confronted with a higher demand than ever for technology that not only intervenes in the immediate wake of a threat, but can serve as a predictive, proactive barrier for safety.

Behind the simple ritual of passing through a metal detector, it may surprise you to learn that a technological revolution is underway in the security sector. Artificial intelligence and machine learning are reshaping the entire construct of metal detection, making it faster, smarter, and more precise than ever before.

Balancing Precision and Demand

Let’s first examine the industry from a birds-eye view. Driven by the factors previously mentioned, the global security screening market is booming. Experts estimate that the metal detection and screening security sector alone is projected to be valued at nearly $10 billion by 2028, driven largely by rising public safety concerns and increasing passenger volumes. Airports, stadiums, arenas, and schools remain under immense pressure to move people quickly without compromising security.

The conundrum that security professionals are faced with is balancing the thoroughness required to ensure safety with convenience and efficiency. We often talk about how secondary screening can fill in the gaps, but the reality is that when you’re faced with tens of thousands of travelers or patrons, you simply can’t open every bag. At the same time, missing a threat can be deadly. This dynamic puts the entire system – from manufacturers, to administrators, to the hands-on staff – in a legitimate bind.

Now, let’s take a closer look at how the technology has historically functioned. Traditional walk-through metal detectors, based on electromagnetic induction, have been relied on as trusted workhorses for decades, capable of processing immense volumes or people at an acceptable pace. Garrett’s walk-through metal detectors, for example, have been the global industry standard for more than 40 years, dating back to when we were commissioned to create our first walk-through detector, the MagnaScanner, for the 1984 Summer Olympic Games in Los Angeles.

Despite their “legacy” status compared to some other advancements in technology, these detectors are still one of the most cost-efficient, high-performance, and stable security solutions on the market. The issue is they have traditionally lacked the intelligence to tell a belt buckle from a weapon, which then leads to the secondary screening process – and the aforementioned delays. These false alarms can lead to human fatigue for security personnel, in turn potentially compromising the effectiveness of their monitoring process – a trickle-down effect that can lead to consequences nobody wants. This is precisely where artificial intelligence and machine learning are stepping in.

The Data Behind Detection

At its core, AI is transforming metal detection from a reactive process to a predictive one. Instead of merely sounding an alarm when metal is detected, AI-powered weapons detection systems can now analyze the shape, density, and position of metallic objects using complex signal pattern recognition.

We’re using AI to achieve levels of functionality for tested metal detection technology that weren’t previously possible. Some voices in the security screening space dismiss metal detection technology as outdated, but they ignore the huge advances in the sophistication of metal detection technology in recent years. By applying these advances in machine learning to metal detection technology, particularly as it contributes to product development, metal detectors have been able to achieve precision previously not thought possible. At Garrett, AI was used to develop an advanced proprietary detection platform using machine learning for our Paragon walk-through metal detector, one of the most widely-used walk-through detectors on the planet. These machine learning algorithms can identify the subtle differences between everyday items—like phones or keys—and potentially dangerous objects, such as knives or firearms.

When you walk through an AI-enhanced detector, it’s not just looking for metal – it’s analyzing thousands of data points and classifying what it sees based on patterns learned from millions of previous trials during development. The result of this is instant, accurate detection of multiple threats and stronger overall security. Our AI platform for Paragon improves the overall accuracy of targets within the portal for single and multiple targets by more than 88% and decreases the false alarm rates over traditional metal detectors on the market by more than 5%.

In large venues, even small efficiency gains have massive impact. A single false alarm can delay dozens of people; multiply that by thousands of guests at a stadium or passengers at an airport, and the disruption adds up quickly. These AI-based systems are helping solve that. The data shows that at several major U.S. airports and sports arenas, walk-through detectors powered by machine learning have cut secondary bag checks and screening times exponentially.

The key is that these detectors don’t just detect—they are packed with intelligence acquired through analyzing thousands of potential scenarios and probabilities of items and environmental factors. The important distinction is that the technology is being provided to security professionals after already being educated with these sophisticated machine learning insights during development, rather than “learning on the job” – this ensures a significantly reduced risk of errors and false readings. By empowering trained professionals to analyze and intelligently apply these insights gathered by machine learning, it guarantees that a more thorough, consistent security standard is being established and implemented across all relevant settings and applications.

Unexpected Applications

There are also applications beyond security for travel and events. Schools and hospitals, for example, are increasingly adopting AI-powered detectors designed to recognize weapons while becoming more proficient at discerning everyday objects, reducing anxiety and bottlenecks. Aesthetics and technology that are conducive to a seamless, unintrusive user experience can go a long way in establishing comfort for patients and visitors while minimizing screening-related confrontations or incidents. Some products may be used in a “stealth mode” where no audible alarming occurs which adds to the comfort of visitors that pass through the screening checkpoint. These “frictionless” systems allow people to walk through without pausing or removing items from their bags—a huge step forward in making security both invisible and effective.

The Human-AI Partnership

It’s essential to note that AI should not replace human security professionals, but support them. The human-AI partnership can help reduce cognitive overload for screeners, who no longer need to manually interpret every beep or scan, however it must be implemented with the proper training and analysis. For example, as part of Garrett’s formal AI policy, we pledge that the results of AI-enhanced design exercises are never used in products through automated or unsupervised means and methods. All AI outputs that are used in our products are vetted by skilled, experienced human agents, and Garrett does extensive product testing in the lab and in the field to ensure that any AI work product is effective and suitable for use in our products. Garrett also does not use AI to build self-learning and self-adjusting security products that may perform at an adequate level when they are installed but then change over time through the influence of exogenous factors that are not prompted by the end user.

AI can help eliminate bias and also establish greater public trust in security systems and professionals. Standardizing weapons detector protocol ensures objective, consistent screening of all individuals, helping alleviate potential tensions and incidents that have long been a concern in manual security checks.

Conclusion: The Quiet Revolution at the Gate

As security technology continues to evolve, the future of walk-through metal detection has the potential to be more impactful and critical than ever thanks to an increased reliance on AI and machine learning. With AI-assisted metal detection, the common pitfalls of traditional metal detection “checkpoints” – delays, false positives, etc. – can be largely alleviated, increasing throughput pace to distinguish between threat and non-threat items at walking speed while operating with more accuracy and precision than ever before.

In an era of ever-increasing human mobility, sophisticated security threats, and heightened scrutiny on security systems, AI-driven metal detection represents a massive step forward for keeping society safe.