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Researchers at Sweden’s Defence Research Agency (FOI) have built WiFireNet, a research deep-learning system that uses Wi‑Fi channel-state information to identify people carrying concealed handguns. The early results show that radio signals contain useful detection clues, but they do not demonstrate a certified airport, stadium or checkpoint screening product.
What the Swedish team built
Erik Persson, David Gustafsson and Tomas Wilkinson at FOI described WiFireNet in an October 2026 paper titled Concealed Handgun Detection using WiFi Signals. The project includes the WiFire dataset, the trained model and source code released for further research.
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At inference time, WiFireNet analyzes Wi‑Fi channel-state information (CSI), not camera pictures. CSI records how the radio channel changes between transmitters and receivers. A person, clothing and an object such as a handgun can alter those measurements. The researchers paired CSI recordings with aligned RGB images so that visual annotations could supervise training; the images provide labels during development rather than serving as the model’s input during detection.
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Why use Wi‑Fi for concealed-weapon detection?
Cameras can miss a handgun hidden by clothing, body position or another obstruction. Metal detectors are established screening tools, but they generally require people to move through a constrained checkpoint one at a time. Wi‑Fi sensing could, in principle, monitor a less restrictive space and add information that a visible-light camera cannot provide.
That describes a potential role, not a replacement. A radio-based system would still need to distinguish a handgun from bodies, clothing, bags and ordinary objects while operating safely around people who have not appeared in its training data.
How WiFireNet works
- Collect CSI: Wi‑Fi measurements capture changes in the radio channel between transmitting and receiving equipment.
- Align supervision data: The WiFire dataset pairs those measurements with synchronized RGB images. Human or computer-generated visual annotations identify the weapon locations used as training targets.
- Train the detector: WiFireNet learns patterns in CSI associated with the annotated handgun examples.
- Evaluate localization: The model is scored by object-detection metrics that measure whether predicted weapon locations overlap the reference locations closely enough.
The released dataset, model and code are intended to let other researchers reproduce, test and improve the approach. They do not turn a Wi‑Fi access point into a ready-to-deploy security scanner.
What the reported accuracy means
The technical report gives the following object-detection results for handguns:
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| Metric | Lenient localization threshold | Strict localization threshold |
|---|---|---|
| Handgun average precision | 72.5% | 53.8% |
| Mean average precision across evaluated classes | 50.3% | 47.1% |
Average precision is not the percentage of people correctly screened. It combines detection and localization behavior over confidence thresholds. The large drop from 72.5% to 53.8% when localization becomes stricter shows that finding a likely weapon and placing its predicted location accurately are different challenges.
The evaluation split signal samples into training and test sets. It did not establish performance in entirely unfamiliar rooms or in separately recorded sessions. A configuration using more pilot subcarriers performed worse overall, and the report says the reason needs further study. Those limitations make the figures useful for judging this experiment, but not for predicting false-alarm or miss rates at a public venue.
How it compares with existing screening approaches
| Approach | Clothing and occlusion | Throughput and space | Weapon specificity and false positives | Privacy | Outside-training performance | Status |
|---|---|---|---|---|---|---|
| WiFireNet-style Wi‑Fi sensing | Potentially less dependent on line of sight; demonstrated only in the reported experiments | Designed to avoid a strict one-person checkpoint, but operational throughput is not established | Handgun localization is promising but still experimental; real-world false-positive rates are unknown | Uses radio measurements rather than camera images at inference, but people’s movement and presence can still be sensitive data | Unproven in unfamiliar rooms and separately recorded sessions | Research prototype |
| Cameras | Can miss weapons hidden by clothing or occlusion | Can observe broader areas without forcing a single-file passage | Visible appearance can support object classification, but lighting, angle and concealment affect results | Records identifiable imagery unless privacy controls are applied | Depends on training data, camera placement and scene changes | Operational technology, with deployment-specific certification and policies |
| Metal detectors | Do not depend on visual visibility, but detect metal rather than weapon shape | Usually require people to pass through a constrained checkpoint one at a time | May alert on harmless metal items and generally need follow-up screening | Can be less visually intrusive than cameras, while still imposing a physical screening process | Performance depends on the detector, settings and screening procedure | Established checkpoint technology |
| Earlier wireless-sensing studies | Varies by experiment | Varies by antenna layout and test area | Results are task-specific and cannot be substituted for WiFireNet results | Varies by collected data | Often tested in limited indoor setups | Research evidence, not a common certification benchmark |
Earlier wireless results need careful interpretation
An IEEE GLOBECOM 2018 study used one transmitting antenna and two multi-antenna receivers in an indoor area of approximately 42 by 39 feet. It reported 86.44 average accuracy for distinguishing metal from non-metal cases. That was a different task, apparatus and metric from concealed-handgun localization, so the number cannot be compared directly with WiFireNet’s average precision.
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Is WiFireNet accurate enough for airports or stadiums?
No evidence in the reported work supports that conclusion. Airport and stadium deployment would require testing across different buildings, radio layouts, crowd densities, clothing, bags, body poses and interference conditions. It would also require an independently recorded evaluation, documented false-positive and false-negative rates, safety procedures, cybersecurity controls, privacy governance and certification by the relevant authority.
The current results establish a promising research direction: CSI can support handgun detection under the tested conditions. They do not establish a dependable percentage of travelers or spectators who would be screened correctly.
Can you buy a Wi‑Fi gun detector?
There is no named consumer detector in this work. FOI released research data, a model and code, not a packaged retail scanner, installation service or certification for security use. Reproducing the experiment would require compatible radio hardware, CSI access, the dataset and technical expertise; the publication does not specify a turnkey product or a universally supported router and adapter combination.
Does it replace metal detectors?
No. The authors position Wi‑Fi sensing as a complementary modality. A future system might combine radio sensing with cameras, metal detectors or human review, but the study does not show that WiFireNet can meet the reliability, throughput, legal and safety requirements of an operational checkpoint.
What happens next?
The released materials allow independent researchers to test generalization, examine why the higher-pilot-subcarrier configuration degraded results and collect data in rooms and sessions not represented in training. The most important next measurements are cross-room and cross-session testing, calibrated false-alarm rates, performance with realistic clothing and bags, and comparisons against established screening procedures under the same conditions.
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