By Joshua Douglas, SVP of Product & Engineering
Many in the industry use “weapons detection” to describe systems that don’t actually detect weapons. They detect metal. All metal. Any metal. While there are other systems designed specifically to detect weapons, the confusion between these capabilities creates operational chaos at thousands of entry points daily.
Traditional walk-through metal detectors operate on a simple principle: electromagnetic field disruption. When ferrous or non-ferrous metal passes through the detection zone, the system induces eddy currents through those metal items. As the metal continues to echo those eddy currents, it disrupts the field. The system registers this disruption and alerts. That’s the entire process. The technology cannot distinguish between a concealed handgun and a set of keys, because both disrupt the electromagnetic field. Both trigger alerts.
This creates a predictable operational pattern. An individual walks through carrying a phone, laptop, keys, belt buckle, and watch. The metal detector alerts. Security personnel conduct secondary screening and no threat is found. The next individual walks through with similar belongings and sets off another alert. Another secondary screening with no threat. This repeats hundreds or thousands of times per day depending on facility size.
Security teams often describe this as “too many false positives” and that’s not technically accurate. The metal detector is functioning exactly as designed; it detected metal and alerted. The problem is that detecting metal presence and detecting weapons are completely different security objectives that require different technical approaches for risk.
The Operational Cost of Confusion
Facilities using traditional metal detectors face the choice of maintaining high sensitivity settings and accepting massive secondary screening volumes, or reducing sensitivity and risking missing actual threats. Neither option solves the underlying problem.
High sensitivity settings detect smaller metal objects, which theoretically improves threat detection. In practice, this means alerting on every small item and everyone carrying normal personal electronics. A corporate office processing 500 employees during morning arrival would have to impact everyone’s privacy and conduct 100% secondary screenings on bags to find potentially zero actual threats. The staff hours consumed investigating non-threats become the dominant security cost both in security personnel hours and every employee attempting to gain access to the facility.
Lower sensitivity settings reduce alert volume by ignoring smaller metal signatures. This improves throughput but creates detection gaps as small concealed handguns might not trigger alerts, and facility security teams then choose between operational efficiency and security effectiveness because the technology forces that tradeoff.
Some facilities post signage requesting individuals remove all metal objects before screening, but this shifts the problem without solving it. Now the facility needs collection bins, table space for divestment, and extended queue areas to accommodate the slowdown. Processing time per individual increases from seconds to minutes and the security checkpoint becomes the operational bottleneck. Not exactly the workplace environment anyone wants to go to daily.
Pattern Recognition Changes the Equation
AI-powered weapons detection operates on different technical principles. Instead of alerting on metal presence, these systems analyze object characteristics to determine threat probability. The shift from presence detection to pattern recognition requires different sensor architecture and real compute baked into the product.
Our systems use magnetic fields similar to traditional metal detectors, but that’s just the starting point. We advance magnetic sensors by approaching transmit and receive as more than just unidirectional and taking data from both sides of an individual withmulti-angle analysis. AI models are trained on thousands of threat and non-threat objects, creating classifications that are matched against near real-time processed data from all sensors simultaneously. The system doesn’t ask “is metal present?” It asks “does this object pattern match known classified threat patterns?”
Because of these classifications performed through deep-learning, shape and material analysis can be examined based on object geometry and response. A laptop has distinct geometric and material characteristics: flat aluminum rectangular profile, uniform thickness, specific aspect ratios,etc. A concealed handgun has different characteristics like irregular steel slide profile, grip angle, barrel length, and trigger guard geometry. The system differentiates these shapes and materials even when both contain similar amounts of metal content.
Density mapping adds another analytical layer. Weapons have specific density patterns based on their construction be it a micro-compact semi-automatic or large frame revolver. A firearm combines high-density conductive components in the barrel and slide with potential lower-density elements in the frame, grip and magazine, which creates a recognizable density signature distinct from consumer electronics, which distribute more uniformly throughout the device.
Placement analysis examines positioning and context. A phone in a pocket presents differently than a weapon concealed against the body. The system evaluates how objects are carried, where they’re positioned, and how they relate to the person’s body, offering a contextual analysis that helps distinguish between carried belongings and concealed threats. Should an irregular location show up for items, this can also create an anomaly for investigation. As the system learns more, the algorithms improve over time.
Real-World Performance Differences
The operational impact shows up in alert rates, too. A traditional metal detector might alert on 60-80%+ of individuals in an office environment where everyone carries phones and laptops. Depending on the sensitivity profile chosen, AI-powered weapons detection could alert on as low as 10-25% of the same population. This is because those solutions are not alerting on metal alone.
The detection rate for actual weapons remains high across both technologies when sensitivity is properly configured with the difference being in what else gets flagged. Traditional metal detectors can’t ignore the laptop. Systems like our Xtract One Gateway can due to the advance sensing and AI
This matters most in environments where personal belongings volume contains a lot of conductive material. Schools where students carry Chromebooks and tablets. Hospitals where staff have multiple electronic devices or patients bring in overnight bags. Corporate offices where employees bring laptops, phones, tablets, and wireless earbuds. These environments become operationally impossible with traditional metal detection because nearly everyone triggers alerts.
The Technical Reality
Building effective threat detection requires training AI models on actual weapons in various concealment configurations. Our engineering team has analyzed thousands of handgun patterns such as different calibers, different carry positions, different concealment methods, and different body types. The models learn what weapons look like when carried by real people in real situations.
This differs from laboratory testing with isolated objects. A handgun by itself on a test bench presents a clear signature. That same handgun concealed in a waistband under clothing, positioned against a body, surrounded by other personal items creates a more complex detection challenge. Our models are trained on complex real-world scenarios, not simplified laboratory conditions.
The model also learns what common personal items look like in those same real-world conditions. Phones in pockets. Keys on belts. Laptops in bags. Tablets in backpacks. The models build libraries of non-threat patterns that can be confidently ignored without secondary screening.
What You’re Actually Trying to Accomplish
The security industry needs to stop conflating metal detection with threat detection. They’re different capabilities requiring different technologies. Metal detectors serve specific use cases where metal presence itself is the concern. For facilities focused on weapons detection while maintaining operational flow, metal presence is not the relevant metric. The operational return justifies the investment in actual weapons detection systems for facilities where traditional metal detection creates unacceptable throughput problems or where security teams spend most of their time investigating non-threats.
If you need to know whether metal is present, metal detectors work fine. If you need to detect weapons without alerting on laptops, you need different technology.