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The artificial intelligence (AI) revolution has made its way into airport X-ray passenger screening systems. By incorporating AI and machine learning (ML) into the process, X-ray screening equipment manufacturers are improving the accuracy, reliability and usefulness of this defense against terrorists.

“The use of artificial intelligence and machine learning (AI/ML) and third-party algorithms to enhance threat detection is on the rise,” said Nicholas E. Ortyl, Leidos’ chief engineer of security solutions. “We use AI/ML in our threat detection algorithms and have begun integrating third-party algorithms in certain use cases.”

A case in point: “Automated prohibited item detection systems (APIDS) are an innovation with much potential for strengthening security and improving efficiency,” said Dr. Yanik Sterchi, a senior research scientist with the Center for Adaptive Security Research and Applications (CASRA). “APIDS capitalize on the advances in AI to detect a variety of prohibited items in X-ray or CT images of passenger baggage.”

How AI Algorithms Assist Passenger X-Ray Screening

To put matters into context, let’s begin by defining what AI algorithms actually are. According to TechTarget.com, “AI algorithms are a set of instructions or rules that enable machines to learn, analyze data and make decisions based on that knowledge. These algorithms can perform tasks that would typically require human intelligence, such as recognizing patterns, understanding natural language, problem solving and decision making.”

Next, let’s narrow our focus to passenger X-ray screening. “For the development of AI algorithms that detect threats, thousands of images are needed that contain a threat,” Sterchi said. “These images together with the location of the threats are fed to a deep neural network, which is then trained to detect the threats. This works similar to how AI can detect objects in photos (e.g., cars, dogs).”

In the realm of passenger screening, “Both conventional X-ray and CT scanners are using increasingly sophisticated AI algorithms to help them ‘recognize’ threats, developed by crunching thousands of X-ray images to build a suite of identifiable traits for objects based on shape, texture, color and density,” said Richard Thompson, vice president of marketing with Smiths Detection. “This approach builds effective screening profiles for dangerous contraband items such as weapons, explosives, and indeed, narcotics.”

It is AI’s ability to process huge volumes of data extremely quickly that makes it so useful in passenger X-ray screening. This capacity allows AI-enabled systems to ‘crunch’ and compare the thousands of images mentioned by Thompson to identify potential threats in a speedy manner.

“At Leidos, we employ deep learning AI methods to examine hidden relationships within the test dataset, aiming to form meaningful associations between threats and the features and shapes of objects,” Ortyl said. “Additionally, advances in dataset generation help ensure these algorithms are built using high quality imagery with precise threat labeling. This approach minimizes the ambiguity between true threats and non-threats.”

This level of fast, deep analysis is something that humans simply cannot do on their own. We’re not alone in this deficiency: “Traditional detection methods typically rely on comparing detected objects against a collection of images and parameters,” said Ortyl. “However, when presented with rotated or oddly positioned detections, these methods could potentially lead to false negatives and false positives due to inherent limitations.”

Maximizing Accuracy by Combining Real-time and Synthetic Data

To analyze data from passenger X-rays in meaningful, insightful and actionable ways, AI algorithms typically draw upon a combination of real-time operational and synthetic data. Real-time operational data refers to the information collected by X-ray systems as they screen passengers. Synthetic data refers to artificially generated data that mimics real-world data created by algorithms or simulations. It resembles real-time data in terms of statistical and physical properties, distribution, and patterns, but it is entirely generated by computer and mathematical models rather than data captured during actual passenger screening.

The reason this combination is being applied in AI-enabled X-ray screening is because synthetic data can be used to augment limited real-world datasets, making them larger and more diverse. These expanded datasets then assist in training more robust and generalized ML (machine learning) assessment models for X-ray screening systems. In turn, this improved training results in more accurate AI-enabled screening in real life.

Combining real-time and synthetic data offers a further advantage to AI-enabled X-ray screening. In these cases where real-time data contains sensitive or private information that cannot be used for ML training, synthetic data can take its place to create surrogate datasets that preserve the statistical properties of the original data while supporting privacy. Other benefits of using synthetic data include lower cost and accessibility as collecting and labeling large amounts of real-world data can be time consuming, expensive, or even impractical.

Of course, generating high-quality synthetic data requires careful modeling and validation. Additionally, synthetic data may not fully capture the complexities and nuances present in real-time data. As a result, it should be used judiciously in conjunction with real data whenever possible.

Benefits and Limits of AI-Enabled X-Ray Analysis

The world’s terrorists are keeping up with the pace of technological change. To keep the flying public safe, passenger screening technology has to do likewise.

“Airports already leverage several threat detection technologies at checkpoints, including millimeter wave people scanners, CT-based or X-ray baggage scanners, and explosive trace detection devices,” Ortyl explained. “But these alone may not be sufficient to stay ahead of threat actors and minimize disruptions to passengers. So, to enhance security operations and support rapidly evolving threats, the integration of AI-based algorithms will likely be a necessity. When properly integrated with checkpoint systems, AI-based algorithms can significantly improve threat detection, reduce human errors in threat detection decisions, and lower false alarm rates from the systems themselves.”

A case in point: By automatically detecting a variety of threats, controlled and prohibited items without impacting the speed of the overall image evaluation, Smith Detection’s AI-enabled iCMORE object recognition software improves operational efficiency at airports. “It has proven to be effectively integrated into normal workflows without impacting the flow of people, bags and cargo,” noted Thompson. At the same time, “iCMORE’s additional detection capabilities provide invaluable support to all image analysts, reducing the burden in decision making, and are particularly helpful for less experienced operators. It is intuitive and easy to use, thus little or no training is required. The increase in operational efficiency also leads to a faster, more streamlined experience of passengers and visitors that have to pass the checkpoint.”

At the same time, today’s AI-enabled X-ray systems should be seen as providing informed support to human security screeners, rather than fully replacing them outright. “AI can assist the officers and increase their detection and improve efficiency,” said Sterchi. “However, the first generation of these AI algorithms cannot yet detect all kinds of threats, which means we must still rely on officers checking for other threats. Beyond that, the consistent performance of the algorithms is both their strength and weakness: they do not tire or get distracted. But if there is a threat they do not detect, they consistently do not detect it, which could be exploited.”

This brings us to a fact about AI that is often missed: massive as they may be, AI-enabled systems are still software systems. As such, they are only as effective as the human thinking that coded their programming. If the coding and data is limited and/or flawed, the same will be true of the AI’s conclusions that are based on it. This is an age-old truth of computer programming known as “Garbage In/Garbage Out.”

“AI-based algorithms are designed to identify patterns in data to create actionable, real-time insights,” Ortyl said. “But the accuracy, speed, and fairness of decisions made by AI-based algorithms are only as good as the quality of the training data and associated ground truth, a characteristic of Leidos’ Trusted Mission AI platform. So, while AI/ML excels in many aspects of image detection, it’s important to acknowledge that human expertise and intuition remain valuable, especially in tasks requiring context, subjective judgment, or ethical considerations.”

Training, Privacy and Other AI Challenges

The power of any AI-enabled system is its ability to “learn” through being exposed to vast amounts of relevant data. This exposure helps the AI to detect and record patterns, providing a basis for the system to accurately assess new data such as X-rays to detect prohibited objects and other threats.

This learning process is known as “training,” and it is the core of any good AI-enabled system. For instance, at Leidos, “Machine learning operations (MLOps) is a development methodology that aims to deploy reliable and efficient machine learning models in production environments,” said Ortyl. “Some of the steps we take include data augmentation to train the models, dataset verification to improve labeling of training data, image preprocessing to improve the fidelity, dataset collection with real human models, and before and after comparisons to predict real-world testing outcomes.”

Of course, using huge datasets derived from human subjects can lead to privacy concerns on the part of the flying public. Fortunately, “AI-driven algorithms prioritize privacy by design, minimizing the storage and retention of unnecessary data and adhering to strict data access controls,” Thompson said. “Furthermore, AI enables proactive threat detection while preserving individual privacy through techniques such as differential privacy, which aggregates and anonymizes data to prevent the identification of specific individuals. By integrating AI into passenger screening protocols, authorities can strike a delicate balance between security imperatives and privacy rights.”

“I do not necessarily see substantial privacy and data security issues regarding personal data when AI algorithms are employed in airport security,” said Sterchi. “The data collection and training of the AI typically happens in a separate step prior to the implementation of the AI system at the airport and afterwards it is not necessary to collect data from passengers. ”This being said, overall cybersecurity is a constant concern for AI-enabled X-ray screening systems, which is why proactive threat detection and protection is a must for system operators.

“In fact, deploying AI for passenger screening presents several challenges,” Ortyl said. “Data privacy and security are key considerations; the collection and protection of AI datasets from breaches and unauthorized access is a high priority. But there is a risk of inherent biases in AI systems, which could potentially lead to unfair treatment of certain passenger groups. Therefore, it’s important for AI algorithms to strive for high accuracy to minimize false positives and false negatives. In addition, AI systems are expected to comply with various national and international regulations, which can be a moving target as new privacy laws and testing guidelines are introduced.”

Other challenges of AI-enabled X-ray screening include integrating AI into existing proprietary systems, which can be complex and costly, plus achieving operational readiness to deploy, maintain and support AI datasets and the systems that use them. AI systems also need regular dataset updates and retraining to stay effective against evolving threats. In turn, this requires ongoing access to high-quality data and robust processes for updating the algorithms.

Worth noting: “The EU regulatory framework requires a recertification for each update that affects detection capability,” said Sterchi. “It will therefore be crucial to have quick recertification processes in place. In the short term, we will have to rely on security officers who are frequently trained for the detection of new and evolving threats.”

Bringing AI Into the Workplace

Even though they have been designed to be intuitive and user-friendly, AI-enabled passenger X-ray screening systems are quite a change for airport screeners accustomed to relying on their own eyes and brains alone. This is why training and transition programs are vital to ensuring their seamless adoption at airports.

“Training people to develop, deploy and support AI in passenger screening should be managed using several key approaches,” Ortyl noted. “At Leidos, our training programs now include fundamental concepts of AI and machine learning, helping security personnel understand how these technologies work and how they can enhance screening processes. This includes hands-on training with AI-based screening tools to understand and interpret the rationale behind AI decisions.”

“At Smiths Detection, we understand that providing the best equipment in the world is only part of the solution: effective training that empowers our customers is crucial,” said Thompson. “To do this we are transforming the way we train people through the use of AI. For instance, our new Virtual Training Center employs Adaptive Learning Technology to personalize every trainee’s learning pathway. It does this by continually monitoring and analyzing data relating to each trainee’s knowledge, skills and learning behavior. The data is then used to adapt the training content to meet individual needs and optimize learning outcomes. This comprehensive data-driven approach not only enhances a trainee’s skills but also contributes to their overall success.”

“CASRA is also developing training modules specifically for security officers working with AI,” Sterchi added. “These focus on learning how to decide whether an AI alarm is correct or not and on searching the bag for the threats that the AI algorithm cannot yet detect.”

Training frontline staff is just part of the process. To stay sharp, the people who manage X-ray screening programs should also undergo regular training on the latest advancements in AI and threat detection, engage in scenario-based training sessions with synthetic data and simulations on a continual basis, and promote collaborative knowledge sharing with others in their profession as well. “These steps will better equip personnel to work alongside AI systems and enhance the overall security and efficiency in passenger screening,” said Ortyl.

It’s also important to strive for transparency in AI models, so that their decision-making processes are understandable and justifiable to their human operators. The reason: Lack of transparency can lead to mistrust and difficulty in justifying AI decisions, especially in cases of false positives or negatives. This is why it is crucial for security personnel to understand how and why the AI reached the conclusions that they are expected to act upon.

“With regard to security officers, our research shows that the deployment of AI for X-ray cabin baggage screening changes their work considerably,” Sterchi said. “As they are expected to resolve the alarms of the AI system, they need a solid understanding of its capabilities and limitations.”

What’s Coming Next

The advances offered by AI-enabled passenger X-ray screening are transforming the very nature of airport security, and bringing it into the 21st century.

So, what is coming next? Well, how about using this same technology to check everyone coming into airport facilities? After all, “We often see airline and retail employees accessing terminals through the same checkpoints as passengers, but hundreds of other personnel working across the airport don’t undergo the same level of entry screening,” said Ortyl. “To bolster public confidence and protect the travelers, airport personnel and the airport infrastructure itself, it is essential to deploy advanced screening practices across the board. Hence applying the same AI-enabled X-ray security screening protocols used for passengers to every individual and their belongings before accessing secure areas of the terminal, where important functions like maintenance, baggage handling and airside fueling occur, is crucial. This is why we expect to see more airports move in this direction and have aligned our capabilities to support this operational shift.”

That’s not all: “The most obvious way forward with regards to automatic object recognition is to add further dangerous goods, prohibited items and other objects to the target recognition list,” Thompson said. “As well, while current AI algorithms are based on 2D images (even if run on CT systems), the development of true volumetric object detection will be a game changer with the growing use of CT-based systems for cabin baggage, hold baggage and freight. Open architecture approaches that enable the sharing of image data and the integration of third-party algorithms can certainly also pave the way to a more secure airport network if, and only if, the industry pulls together a common framework to ensure open architecture platforms operate safely and compliantly.”

“With regard to AI in X-ray cabin baggage screening, we can certainly expect that these algorithms will improve over the next few years,” said Sterchi. “I would therefore expect that security officers will no longer have to inspect all X-ray images. Instead, they will focus on the more difficult cases and APIDS’ alarm resolutions. Moreover, the screeners could also have a role for the continuous improvement of the AI systems. After all, a decrease in more easy tasks and increase in difficult tasks that require well-designed training to achieve the necessary expertise has been observed in many industries where automation was introduced.”

“The way forward is increased collaboration and constructive engagement not only between suppliers, but between suppliers, airports and regulators,” Thompson concluded. “Only through working more closely together will the industry realize the opportunity for a more integrated, data-driven and ultimately secure future.”

The bottom line: AI-enabled passenger X-ray screening is a major step forward for the airport security sector, and one that represents real progress in the fight against terrorists and others who want to violate airport security. As such, airports large and small should consider adding AI to their security arsenals, because this technology can make a real difference.

The first case of air rage was reported in 1947 on a flight from Havana to Miami and involved an intoxicated passenger assaulting another passenger. Air rage and unruly passenger behavior reached a record high in 2021 when the FAA received 5,973 reports from airlines, an increase of 492% from 2020. Customer service training, particularly for forward-faced staff such as in-flight, ticket, and gate agents, plays a crucial role in preventing such incidents. Resolving passenger complaints is vital to maintaining customer satisfaction and contributes to the airline’s success. When a flight is delayed or canceled, passengers may become frustrated, but the staff’s conflict resolution training can de-escalate tensions and provide solutions that can mitigate negative experiences. Staff ability to calmly resolve a conflict prevents the situation from escalating and ensures the safety and security of other passengers, making them an integral part of the operation.

Conflict resolution skills are essential in the workplace, too, as airline operations require close cooperation among many teams, pilots, cabin crew, ground crew, customer service staff, contractors, and more. Maintaining a professional and harmonious working relationship using conflict resolution skills will ensure a smooth and efficient operation. Conflicts resolved constructively in the work environment help build a positive organizational culture; job satisfaction will be higher while turnover rates are reduced. Effective conflict resolution skills promote teamwork and collaboration and are essential in the aviation industry due to the complex nature of the work.

Conflict resolution skills can be shared with staff through training. These skills include active listening, effectively communicating, showing empathy, being creative and problem-solving, controlling your emotions, remembering to be patient, negotiating, assertiveness when necessary, and always being open to adjusting your approach based on the situation and feedback from others.

Developing and practicing conflict resolution skills will reduce the need for law enforcement involvement at the gate or on the airplane, it will improve relationships with customers and fellow staff, and will help you maintain a more positive, professional work environment. Aviation is a highly stressful career, but the stress does not have to define who you are. By actively engaging in conflict resolution, you, as a staff member, can contribute to a more positive and professional work environment, fostering a sense of motivation and commitment among the staff.

If you want to reduce your stress while ensuring you are operating within the highly regulated aviation field, learn conflict resolution skills and put them to work every day. You can use conflict resolution skills to improve communication between pilots, flight attendants, ground staff, maintenance, and management. Improved communication will reduce misunderstandings while fostering a positive work environment. By mastering conflict resolution, you can take control of your work environment and reduce your stress levels, empowering yourself in your role.

Airlines should develop and implement conflict resolution training programs for staff, focusing on the skills I mentioned above: active listening, empathy, negotiation, and emotional control. This will prevent an unruly passenger incident from escalating.

As a staff member, if you witness a dispute at the airport, office, or on board a plane, you can play a crucial role as a mediator. By offering to listen and help resolve the conflict quickly and fairly, you can contribute to a more harmonious and professional work environment.

Your skills in identifying, managing, and resolving an unruly passenger incident will lead to a better passenger experience for everyone, including the unruly person. Your dedication to ensuring that respect is shown to the passenger while communicating clearly and openly will let others see you as a leader, and they will know you are in charge. Your positive, professional, yet assertive behavior will reduce the likelihood that the unruly passenger situation will escalate. You will feel better at the end of the day knowing that your actions made a difference in the lives of many.

In the office, address conflicts promptly and implement clear policies and procedures that show fairness and a sense of security. By actively promoting diversity and inclusion through conflict resolution, you can make a significant contribution to creating a more harmonious and productive workplace, making you feel valued and respected.

Conflict resolution skills are vital to maintaining an efficient, on-time airline. A positive organizational culture that fosters safety, customer satisfaction, and employee relations will ensure a safe airline. Conflict resolution training and strategies will create a pleasant environment for employees and customers.

About the Author

Frederick Reitz is an aviation security specialist, private pilot, licensed Florida mediator, and co-founder and managing director of SAFEsky, a leading aviation security company. His career spans 20 years in aviation security and 15 years in law enforcement. His achievements include creating the security programs for two start-up airlines and is dedicated to this crucial aspect of the industry. Frederick’s expertise in risk and threat analysis, emergency response planning, fingerprint technology integration and security protocol implementation has positioned him as a leading authority with extensive experience in regional and international airlines. He holds a Bachelor of Science degree in aviation management and a master’s in aeronautical science. He is currently pursuing a Ph.D. in conflict analysis and resolution. As a thought leader, he passionately shares his knowledge and insights as a speaker on aviation security at national and international forums. His presentations, whether addressing flight attendants, ground security coordinators, pilots or police officers, reflect his profound understanding of aviation safety and the challenges diverse industry professionals face. He can be reached at rick@safesky.us.

According to a January 12, 2024, news release issued by the U.S. Transportation Security Administration, this agency screened a record 858+ million passengers in 2023. Although the TSA news release doesn’t say it directly, it is reasonable to assume that most or likely all these people were checked using body scanners for concealed weapons and contraband. These screenings prevented 6,737 firearms — 93% of them loaded — from getting into the secure areas of the airports and onboard aircraft.

Clearly, airport body scanners are more important than ever for keeping the world’s airports and airliners safe. This is why knowing the trends driving this technology today and into the future is so important for this in the aviation industry.

To get a handle on these trends, TSI magazine staged a ‘virtual roundtable’ with three experts with major body scanner manufacturers. They are Nik Karnik, vice president and division manager at Leidos; Harald Jentsch, head of Aviation Security Screening for Rohde & Schwarz; and Rob Keeler, managing director of LINEV Systems UK. Here is what the experts had to tell us.

TSI: How have airport body scanners evolved and improved over the years?

Nik Karnik: As the provider of the first millimeter wave people scanner over 20 years ago, Leidos keeps continuous improvement in passenger screening a top priority. Today, airports expect high throughput and effective threat detection with better accuracy across a broad range of metallic and non-metallics. They also want minimal false positives to reduce the frequency of secondary pat-downs and intervention by security personnel.

To keep up with evolving threats and customer needs, our newest system has moved away from traditional detection methods based on image similarity and features extracted by human intuition. We employ deep learning-based, artificial intelligence algorithms that utilize gender-neutral threat detection. Additionally, our advancements in dataset generation ensures that these algorithms are built using high quality imagery with precise threat labeling, reducing the ambiguity between true threats and non-threats.

Harald Jentsch: Security scanners have evolved significantly over the last decade, largely driven by advancements in artificial intelligence and machine learning technologies. Previously, checkpoint security screening relied heavily on X-ray scanners for baggage and walk-through metal detectors as well as physical pat-downs for people screening. Limited to the detection of concealed metallic items, it wasn’t possible to detect person-borne concealed explosives, ceramic knives, printed weapons, or contraband.

This changed with the introduction of millimeter wave (mmW) security scanners more than 15 years ago, which detect a far wider range of threats and prohibited items. Another change from legacy body scanners is the physical design of the R&S QPS201 Ultra High-Definition (UHD) body scanner. The system has an open design and is fully electronic — comprised of two solid-state flat panels. This means on-person security screening is no longer confined to a narrow cylindrical scanner and is made more comfortable with an easy hands-down scan pose. Further, the QPS201’s large, open pathway is an accessible path for passengers with disabilities and provides additional egress for emergency evacuation situations.

Rob Keeler: The evolution of airport body scanners has been a fascinating journey marked by technological advancements tailored to address emerging security threats in the realm of aviation.

Initially, aviation security measures were minimal as threats were virtually nonexistent in the 1950s. However, with the rise of terrorist hijackings involving metallic weapons such as guns and knives, passenger screening became imperative.

The response to these threats led to the development of Walk-Through Metal Detectors (WTMD) and Handheld Metal Detectors (HHMD), serving as the primary screening tools for person-borne threats for years. While effective against metallic threats, these detectors proved insufficient against non-metallic threats, highlighted by incidents like the attempted bombing by the “underpants bomber.”

To overcome the limitations of WTMD, exploration into new technologies capable of detecting both metallic and non-metallic threats ensued. Initial trials involved Backscatter X-ray Body Scanners, showing promise in detecting metallic and non-metallic items on a person’s body but were unable to identify internal threats. However, concerns regarding ionizing radiation’s potential health hazards led to their removal from use at Manchester Airport.

Subsequently, investment shifted towards the development of alternative technologies, notably active millimeter wave (mm-wave) scanners. These scanners, currently deployed in airports, offer robust detection capabilities. Nonetheless, as the threat landscape evolves, future advancements will likely be necessary to address new techniques that may evade existing detection methods.

TSI: Why are backscatter x-ray units being replaced by millimeter wave scanners?

Jentsch: Body scanners, utilizing backscatter or transmission X-ray technologies, have been replaced by mmW-based scanners in aviation security screening, and many other use cases due to safety and privacy concerns. Millimeter wave imaging has advanced significantly and delivers safe and accurate people screening and as millimeter waves cannot penetrate skin, scanners like those produced by Rohde & Schwarz pose no health risks to travelers and security operators

Keeler: The replacement of backscatter X-ray units by millimeter wave scanners stems from a confluence of factors encompassing privacy concerns, fears surrounding ionizing radiation, and the acceptable detection efficacy offered by millimeter wave technology.

Initially, privacy issues hindered the widespread adoption of backscatter X-ray scanners. However, the subsequent apprehension regarding ionizing radiation further sidelined X-ray technology.

Regulations governing the use of ionizing radiation, such as Euratom, IRR17, and JOPA2004, are predicated on the Linear No Threshold (LNT) model, which asserts that any exposure to radiation carries inherent health risks. Consequently, X-ray systems encounter stringent limitations unless their deployment can be justified by a significant probability of threat detection.

Despite emerging evidence supporting hormesis — the notion that low doses of ionizing radiation may confer health benefits — entrenched fears perpetuated by the LNT model continue to influence regulatory frameworks. This dichotomy underscores a complex interplay between scientific understanding and regulatory policy within the realm of radiation safety.

Given the low likelihood of encountering terrorists during routine passenger screenings, the adoption of millimeter wave scanners, which operate without ionizing radiation, has become the preferred choice. These scanners offer detection capabilities while circumventing the regulatory constraints associated with X-ray technology.

It is noteworthy that X-ray Transmission Body Scanners persist in certain contexts, such as customs inspections and prison facilities, where there is a reasonable probability of detecting internal threats such as drug smuggling. In such instances, regulatory justifications for X-ray system usage have been granted under specific frameworks, such as JOPA2004.

In conclusion, the transition from backscatter X-ray units to millimeter wave scanners reflects a multifaceted interplay between technological capabilities, regulatory considerations, and evolving perceptions of radiation risk. This shift underscores the dynamic nature of aviation security measures and the imperative of balancing efficacy with safety and privacy concerns.

TSI: What role can technologies such as AI play in making airport body scanning more accurate and efficient?

Jentsch: Artificial Intelligence (AI) and other methods are being used to enhance accuracy and efficiency in all security screening technologies. But AI and other advanced methods are only as good as the quality and quantity of data collected and processed by a security scanner.

The internal resolution of a body and its data quality are critical elements to enable AI delivery of performance improvements. In this way, AI-powered automated threat recognition (ATR) software enables the automated detection of person-borne threats and anomalies more effectively than ever to deliver reduced false positives and improved detection rates. These AI algorithms can be trained to recognize a wide range of current and emerging threats to improve security and enhance checkpoint throughput and operational efficiency of people-screening technology in aviation security.

In addition, AI has enabled the development of new capabilities to make screening more inclusive and easier for more passengers, as it has made it possible to implement gender nonbinary screening and enhanced detection performance powered by AI, making screening more accurate and faster for all passengers. Future enhancements that AI technology may deliver include the ability to make screening of passengers with disabilities less invasive and may someday be able to screen passengers in wheelchairs.

Keeler: The integration of AI into airport body scanning holds immense promise for enhancing accuracy and efficiency, yet it also presents challenges that must be navigated judiciously.

AI-based technologies excel in scenarios where detection tasks are well-defined, and threats can be clearly classified and marked during training. This is particularly advantageous in scenarios such as scanning for assembled firearms in non-dense baggage. However, challenges arise when dealing with disassembled weapons or when scanning for a wide range of potential threats simultaneously where the AI has not been ‘trained’ for those threats. The false alarm rate becomes a critical factor in determining the efficacy of machine vision, with low false alarms and high detection probability being prerequisites for its practical application.

In the context of body inspection, AI offers significant advantages, especially in conjunction with transmission X-ray technology. The relatively uniform nature of the human body simplifies the task for AI algorithms, particularly in discerning between biological structures and potential threats. By focusing on identifying non-human objects on the body, AI can streamline the inspection process, allowing operators to concentrate on anomalies rather than conducting exhaustive internal body analyses. While training operators to interpret complex biological structures can be challenging, machine vision simplifies this task significantly.

However, the integration of AI into security screening processes is not without its pitfalls. Operators may become overly reliant on AI, assuming that the absence of an alarm indicates the absence of threats. Yet, AI systems have inherent limitations, primarily dictated by the parameters of their training data. If an AI model is not trained to identify specific threats, it may fail to trigger an alarm even in the presence of a threat. This potential for oversight underscores the importance of incorporating complementary techniques such as Threat Incident Projection (TIP) to mitigate operator complacency.

In conclusion, while AI holds immense potential for revolutionizing airport body scanning, its implementation must be approached with caution. By leveraging its strengths in detecting predefined objects and complementing it with strategies to mitigate human error, AI can indeed enhance security screening efficiency and accuracy. However, careful consideration of its limitations and potential risks is imperative to ensure its integration yields positive outcomes in the realm of aviation security.

TSI: Can technology reduce the number of people needed for security screening work, at a time when help is hard to find?

Jentsch: Technology can indeed reduce the number of staff required in security screening operations. Advanced automated detection software can remove much of the burden from security operators by reducing false and nuisance alarms. This results in a higher pass-rate which reduces time-consuming alarm resolution pat-down procedures.

Fewer alarms mean fewer staff are required to resolve alarms. Beyond increased security and operational efficiencies, advanced technology also improves the passenger checkpoint security screening experience. Additionally, as part of the DHS Screening at Speed (SaS) Program, Rohde & Schwarz and its partners have developed technology that can automate and deliver self-service security screening. It utilizes the QPS201 with a passenger guidance system and access control gating solution, requiring minimal security staff to mainly provide direction and if required, to resolve alarms. As widely reported in major media coverage this month, this solution has been deployed at Harry Reid Las Vegas International Airport at the Transportation Security Administration’s (TSA) Innovation Checkpoint for a six-month ongoing trial assessment. It was reported that this demonstration system can substantially reduce the number of screening operators from twelve people down to five for these trials.

Keeler: The role of technology in reducing the number of personnel required for security screening work, particularly during times of scarcity, is a multifaceted consideration influenced by task complexity and the efficacy of technological solutions.

While technology, particularly AI, holds promise in streamlining security screening processes, its implementation hinges on the complexity of the task at hand. AI’s effectiveness is most pronounced when tasked with aspects of the screening process where it can deliver close to 100% probability results with a negligible false alarm rate. This doesn’t necessarily translate to a reduction in personnel numbers but rather a refinement in the qualifications required. AI can effectively guide operators, simplifying their tasks and enhancing overall efficiency. However, it’s premature to rely solely on machine vision algorithms given current technological limitations.

Beyond mere reductions in staffing, the crux lies in technology’s potential to enhance the Probability of Detection (PoD) of threat items. Irrespective of the workforce size, certain threats may evade detection through manual searches, particularly those concealed internally.

A system boasting a high PoD could indeed lead to a significant reduction in staffing requirements. However, this must be balanced with the risk of a heightened False Alarm Rate (FAR), wherein alarms are triggered for non-threatening items. Striking this balance is critical, often requiring optimization of the Receiver Operating Curve (ROC) to find equilibrium between PoD and FAR.

The impact of technology on staffing levels is palpable in people screening, where technologies such as millimeter-wave body scanners play a pivotal role. Depending on the FAR required to achieve an acceptable PoD, the need for manual pat-downs may vary. High FAR scenarios may necessitate numerous manual pat-downs, potentially escalating staffing requirements, especially considering same-sex pat-down protocols. Conversely, a low FAR at the requisite PoD could effectively reduce the demand for personnel.

In conclusion, while technology presents opportunities to enhance security screening efficiency and reduce staffing requirements, its implementation must be carefully calibrated to balance PoD and FAR considerations. Achieving this equilibrium is crucial to optimizing security screening processes in the face of staffing challenges.

TSI: What other trends are affecting the airport body scanner market, in terms of products and client needs?

Karnik: We expect to see automated, self-screening checkpoints more prevalent in the future. This requires body scanners to be not only accurate and fast, but easy for even the casual traveler to use. These checkpoints will also need enterprise software to enable and aid remote monitoring. With a holistic management solution, capable of integrating screening technologies across manufacturers, airports will have access to vital security data within a single interface. This could include everything from traveler verification data and flight data to the data collected from people and baggage scanners.

Jentsch: As passenger volumes continue to increase at airports around the world and as airport security staffing remains challenged, the airport body scanner market is being driven by the need for faster, more efficient and minimally intrusive security screening for all passengers.

In addition, technology needs to adapt to process a diverse range of passengers. The number of aging and disabled travelers is growing and there is the opportunity, with new technology, to make security screening easier and faster for all travelers.

Working with a wide range of our customers, we are being called upon to optimize the R&S QPS systems’ screening capability for specific use cases. At Rohde & Schwarz we are able to develop specialty software to adapt, both the R&S QPS201 and the R&S QPSWALK scanning software, for specific use cases to, for example, facilitate airport employee screening and enable other high-volume security screening operations.

Keeler: The airport body scanner market is undergoing significant shifts influenced by a multitude of factors ranging from technological advancements to evolving client needs and regulatory standards.

On the technological front, the prevalence of airport security procedures presents challenges in terms of efficiency and efficacy. While some airports still rely on traditional metal detectors, many have adopted state-of-the-art millimeter-wave scanners, which are increasingly becoming the preferred choice due to their superior detection capabilities when compared to metal detectors. Moreover, there’s a growing trend towards non-stop inspection procedures, streamlining the scanning process to minimize delays and queues, akin to passing through a metal detector seamlessly.

However, mm-wave scanners have limitations, particularly in their inability to provide internal imaging. This shortfall has paved the way for the potential resurgence of transmission X-ray body scanners, particularly those employing ultra-low doses of radiation. The legality of such scanners is a critical consideration, with regulatory standards like ANSI 43.17-2009 governing radiation safety in the United States. While some countries have stringent regulations against X-ray usage, advancements in technology and safety measures may pave the way for broader acceptance.

Moreover, the dynamic nature of aviation security demands equipment capable of addressing both existing and emerging threats. Suppliers are thus focused on enhancing the Probability of Detection (PoD) while reducing the False Alarm Rate (FAR) and streamlining scan duration to enhance throughput and passenger experience.

In essence, the future of airport body scanning lies in striking a delicate balance between technological innovation, regulatory compliance, and addressing evolving security threats. Advancements in AI, coupled with improvements in scanning technology, hold the promise of revolutionizing airport security, ensuring the safety and efficiency of air travel for passengers worldwide.

TSI: What body scanning products are you offering that address these trends and needs?

Jentsch: Unlike legacy body scanners, Rohde & Schwarz has developed on-person screening technology specifically to meet the needs of modern security screening operations. Both of our solutions utilize safe, high-definition mmW technology and both of our products utilize sophisticated AI and machine learning to continually improve the detection performance for increased throughput and improved passenger experience.

Our flagship solution, the R&S QPS201 Ultra High Definition (UHD) scanner, is widely deployed world-wide and has redefined the standard for on-person screening. The R&S QPS Walk2000 is our newest product in security scanners and utilizes ultra-wideband technology to deliver unmatched high-fidelity screening without the need for passengers and/or airport or other employees to divest outerwear and other clothing. The scanner screens continuously, without the need to stop, as required in typical checkpoint screening operations. For the first time, the R&S QPS Walk2000’s high-volume and high-definition on-person security screening technology delivers the capabilities security operators need.

Keeler: Since pioneering the world’s first Security Transmission X-ray Body Scanner in 1999, LINEV Systems has remained dedicated to continuous enhancement in design, development, and manufacturing of X-ray Transmission Body Scanners. These scanners are currently deployed across various sectors, including customs, prisons, diamond and precious metal mines, among others. LINEV Systems has supplied well over 1,000 units of different types, including torso, whole body, dual-view, and portable versions, all of which are readily available.

We offer unique low-dose transmission X-ray body scanners for airport security and customs control. We provide two main modifications: the full-body scanner, CLEARPASS, and the partial-body compact scanner, CLEARPASS CI. Both models are compatible with ANSI43.17-2009 regulations. Additionally, they boast extensive AI and machine vision functionality, offering very high throughput alongside the best X-ray image quality available on the market.

All of our body scanners feature AI and machine vision functionality. Threat visualization can be configured to display detailed information or in a privacy protection mode, similar to mm-wave scanners. Furthermore, our transmission X-ray body scanners are equipped with machine vision algorithms capable of distinguishing between different materials, such as clothes (green), electronics (blue), weapons (red), or unidentified objects (brown).

LINEV Systems is eagerly anticipating the alignment of the aviation market and radiation regulators with the significant benefits their products offer to security, and even to people’s health.

TSI: Finally, what advances are on the horizon for airport body scanners?

Jentsch: The future of body scanners promises even greater efficiency and comfort. For example, as technology continues to evolve, walk-through or walk-by, on-person screening will be possible in airport checkpoints and meet the requirements of other high volume security operations like the screening of aviation workers. Moreover, as scanner technology and AI make further advancements, mmW systems will become more precise with far fewer false alarms and better able to detect a wider range of smaller threats and contraband. This means that there will be less reliance for security operators and passengers may have self-service security screening options available to them.

Another key development on the horizon that passengers will love is the addition of footwear scanning to body scanners. Rohde & Schwarz and other manufacturers are exploring shoe scanner technologies that will eliminate the need for passengers to remove their shoes for security screening.

The challenges of screening people in airport security screening and the myriad of other use cases and missions are well known and Rohde & Schwarz has developed body scanner technology to address the needs of aviation security screening operations, but we are continuing to innovate. We have a few on-going research and development initiatives underway to add long-awaited capabilities to make on-person security screening more accurate, faster and easier for all passengers and security operators.