Automation in airport security X-ray screening of cabin baggage: Examining benefits and possible implementations of automated explosives detection

Visual inspection problem-solving strategies at different experience levels

Airport security screening is a visual inspection task comprising search and decision. Problem solving is used to support decision making. However, it is not well understood. This study investigated how airport security screeners employ problem solving during x-ray screening, and how strategies change with experience. Thirty-nine professional security screeners were observed performing x-ray screening in the field at an Australian International Airport. Video and eye-tracking data were collected and analysed to explore activity phases and problem-solving strategies. Less-experienced screeners performed more problem solving and preferred problem-solving strategies that rely on visual examination without decision support or that defer decision making, compared to more-experienced screeners, who performed efficient and independent strategies. Findings also show that screeners need more time to develop problem-solving skills than visual scanning skills. Screeners would benefit from problem-solving support tools and intensified training and mentorship within the first six months of experience to advance problem-solving competencies.

The road to smart airports: Bibliometric analysis of digital transformation by using R language

BACKGROUND

The accumulated knowledge has led to a state of misunderstanding about the precise meanings of digitalization, and a precise framework to define smart airports is still missing.

OBJECTIVE

This study aims to reveal the current status and future direction of smart airports and digital transformation in the academic literature and to provide a comprehensive definition for smart airports.

METHODS

The identified keywords were searched in the Web of Science database covering the years 1989-2024 and a total of 372 studies were found. These studies were then analyzed using Bibliometrix (R package).

RESULTS

We determined that the most influential academic source on the themes is the Journal of Air Transport Management, and the collaboration index in the literature is three. While conferences are the most productive sources in this field, academic journals are mostly cited in studies. Academic studies typically employ and evaluate "performance" and "model," "impact" and "air," and "economic development" and "location" in tandem, despite the distinction between technological and managerial issues.

CONCLUSION

In the light of the findings, the definition of a smart airport can be "an airport ecosystem where personalized service is provided to users by using Industry 4.0 technologies on the basis of big data analysis and real-time sharing between objects; digitalization is turned into a holistic organizational culture starting from top management to cover all personnel; the decision-making process is carried out autonomously within the entire airport operation network; and the main goal of competitive advantage and high-level user experience is provided uninterruptedly."

Evaluating levels of automation with different feedback modes in an assistive robotic table clearing task for eldercare

This paper focuses on how the autonomy level of an assistive robot that offers support for older adults in a daily task and its feedback affect the interaction. Identifying the level of automation (LOA) that prioritizes older adults' preferences while avoiding passiveness and sedentariness is challenging. The feedback mode should match the cognitive and perceptual capabilities of older adults and the LOA. We characterized three LOAs and paired them with two modes of feedback in a human-robot collaborative task. Twenty-seven older adults participated in evaluating the LOA-feedback variations in a mixed experimental design, utilizing an experimental setup of an assistive robot in a table clearing task. The quality of the interaction was evaluated with objective and subjective measures. The combination of high LOA with voice feedback improved the overall interaction when compared to other LOA and feedback combinations. This study emphasizes the importance of appropriate coupling of LOA and feedback for successful interaction of the older adults with an assistive robot.

The Modus Operandi of Terrorist Attacks Using Improvised Explosive Devices In Landside Zones From 2001 To 2018

The paper concerns the analysis of changes in the modus operandi of terrorist attacks in the landside zones, in order to identify the areas that are most vulnerable to attack using IEDs. Attacks carried out in the passenger terminal and in the car parks were analysed.

Measuring the Efficiency of Automation-Aided Performance in a Simulated Baggage Screening Task

OBJECTIVE

The present study replicated and extended prior findings of suboptimal automation use in a signal detection task, benchmarking automation-aided performance to the predictions of several statistical models of collaborative decision making.

BACKGROUND

Though automated decision aids can assist human operators to perform complex tasks, operators often use the aids suboptimally, achieving performance lower than statistically ideal.

METHOD

Participants performed a simulated security screening task requiring them to judge whether a target (a knife) was present or absent in a series of colored X-ray images of passenger baggage. They completed the task both with and without assistance from a 93%-reliable automated decision aid that provided a binary text diagnosis. A series of three experiments varied task characteristics including the timing of the aid's judgment relative to the raw stimuli, target certainty, and target prevalence.

RESULTS AND CONCLUSION

Automation-aided performance fell closest to the predictions of the most suboptimal model under consideration, one which assumes the participant defers to the aid's diagnosis with a probability of 50%. Performance was similar across experiments.

APPLICATION

Results suggest that human operators' performance when undertaking a naturalistic search task falls far short of optimal and far lower than prior findings using an abstract signal detection task.

Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images

Threat detection in X-ray security images is critical for preserving public safety. Recently, deep learning algorithms have begun to be adopted for threat detection tasks in X-ray security images. However, most of the prior works in this field have largely focused on using image-level classification and object-level detection approaches. Adopting object separation as a pixel-level approach to analyze X-ray security images can significantly improve automatic threat detection. In this paper, we investigated the effects of incorporating segmentation deep learning models in the threat detection pipeline of a large-scale imbalanced X-ray dataset. We trained a Faster R-CNN (region-based convolutional neural network) model to localize possible threat regions in the X-ray security images on a balanced dataset to maximize detection of true positives. Then, we trained a DeepLabV3+ model to verify the preliminary detections by classifying each pixel in the threat regions, which resulted in the suppression of false positives. The two models were combined in one detection pipeline to produce the final detections. Experiment results demonstrate that the proposed method significantly outperformed previous baseline methods and end-to-end instance segmentation methods, achieving mean average precisions (mAPs) of 94.88%, 91.40%, and 89.42% across increasing scales of imbalance in the practical dataset.

Visual search behavior and performance in luggage screening: effects of time pressure, automation aid, and target expectancy

Visual inspection of luggage using X-ray technology at airports is a time-sensitive task that is often supported by automated systems to increase performance and reduce workload. The present study evaluated how time pressure and automation support influence visual search behavior and performance in a simulated luggage screening task. Moreover, we also investigated how target expectancy (i.e., targets appearing in a target-often location or not) influenced performance and visual search behavior. We used a paradigm where participants used the mouse to uncover a portion of the screen which allowed us to track how much of the stimulus participants uncovered prior to their decision. Participants were randomly assigned to either a high (5-s time per trial) or a low (10-s time per trial) time-pressure condition. In half of the trials, participants were supported by an automated diagnostic aid (85% reliability) in deciding whether a threat item was present. Moreover, within each half, in target-present trials, targets appeared in a predictable location (i.e., 70% of targets appeared in the same quadrant of the image) to investigate effects of target expectancy. The results revealed better detection performance with low time pressure and faster response times with high time pressure. There was an overall negative effect of automation support because the automation was only moderately reliable. Participants also uncovered a smaller amount of the stimulus under high time pressure in target-absent trials. Target expectancy of target location improved accuracy, speed, and the amount of uncovered space needed for the search.Significance Statement Luggage screening is a safety-critical real-world visual search task which often has to be done under time pressure. The present research found that time pressure compromises performance and increases the risk to miss critical items even with automation support. Moreover, even highly reliable automated support may not improve performance if it does not exceed the manual capabilities of the human screener. Lastly, the present research also showed that heuristic search strategies (e.g., areas where targets appear more often) seem to guide attention also in luggage screening.

Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images

The inherent imbalance in the data distribution of X-ray security images is one of the most challenging aspects of computer vision algorithms applied in this domain. Most of the prior studies in this field have ignored this aspect, limiting their application in the practical setting. This paper investigates the effect of employing Generative Adversarial Networks (GAN)-based image augmentation, or image synthesis, in improving the performance of computer vision algorithms on an imbalanced X-ray dataset. We used Deep Convolutional GAN (DCGAN) to generate new X-ray images of threat objects and Cycle-GAN to translate camera images of threat objects to X-ray images. We synthesized new X-ray security images by combining threat objects with background X-ray images, which are used to augment the dataset. Then, we trained various Faster (Region Based Convolutional Neural Network) R-CNN models using different augmentation approaches and evaluated their performance on a large-scale practical X-ray image dataset. Experiment results show that image synthesis is an effective approach to combating the imbalance problem by significantly reducing the false-positive rate (FPR) by up to 15.3%. The FPR is further improved by up to 19.9% by combining image synthesis and conventional image augmentation. Meanwhile, a relatively high true positive rate (TPR) of about 94% was maintained regardless of the augmentation method used.

Automation reliability, human-machine system performance, and operator compliance: A study with airport security screeners supported by automated explosives detection systems for cabin baggage screening

Using a simulated X-ray screening task, we tested 122 airport security screeners working with the support of explosives detection systems for cabin baggage screening (EDSCB) as low-level automation. EDSCB varied systematically on three automation reliability measures: accuracy, d', and positive predictive value (PPV). Results showed that when unaided performance was high, operator confidence was high, and automation provided only small benefits. When unaided performance was lower, operator confidence was lower, and automation with higher d' provided large benefits. Operator compliance depended on the PPV of automation: We found lower compliance for lower PPV. Automation with a high false alarm rate of 20% and a low PPV of .3 resulted in operators ignoring about one-half of the true automation alarms on difficult targets-a strong cry-wolf effect. Our results suggest that automation reliability described by d' and PPV is more valid than using accuracy alone. When the PPV is below .5, operators should receive clear instructions on how to respond to automation alarms.

Interpol review of detection and characterization of explosives and explosives residues 2016-2019

This review paper covers the forensic-relevant literature for the analysis and detection of explosives and explosives residues from 2016-2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/Resources/Documents#Publications.

Some cues are more equal than others: Cue plausibility for false alarms in baggage screening

This study investigated the effects of cue plausibility in a baggage screening task. 120 participants had to indicate whether a prohibited item was present in a series of grey-scaled X-ray images of baggage. They were assisted by a support system, which pointed at the location of a suspicious object. A 2 × 2 × 2 between-subjects design was used. Cue plausibility for false alarms (i.e. how the cued object was similar to a prohibited item) and support system reliability were manipulated at two levels (high/low). Furthermore, half of participants were provided with a rationale about automation failures (RAF) to reduce their negative impact on trust and performance. The results showed lower performance and more compliance with automation suggestions when cues were implausible than plausible. The RAF increased the response time and did not improve detection performance. Overall, this suggests that effective (computer-based) training is needed to reduce the negative effect of plausible cues.

Work design for airport security officers: Effects of rest break schedules and adaptable automation

This study investigated whether there is empirical support for the current EU regulation mandating breaks of at least 10 min after each period of 20 min continuously reviewing X-ray images in airport security screening. As a second goal, it examined whether providing more autonomy to airport security officers (in the form of spontaneous rest breaks and adaptable automation) would improve their performance and subjective state. Seventy-two student participants had to indicate the presence (or absence) of a threat item (either a gun or a knife) in a series of grey-scaled X-ray images of cabin baggage. Three work-rest schedules were examined: spontaneous breaks (i.e. participants could take breaks at any time), two 5-min breaks and two 10-min breaks during a 1-h testing session. Furthermore, half of the participants were assisted in their task by an adaptable support system offering three levels of automation: (1) no support, (2) cues indicating the presence of a potential threat item, and (3) cues indicating the exact location of a potential threat item. Results showed no performance differences between break regimes, which suggests that there may be viable alternatives to the current EU regulations. It also emerged that providing participants with adaptable automation did not lead to better detection performance but resulted in a less positive response bias than participants without automatic support. Implications for current aviation security regulations are discussed.

Expertise, Automation and Trust in X-Ray Screening of Cabin Baggage

X-ray screening of passenger baggage is a key component in aviation security. The current study investigated how experts and novices performed in an X-ray baggage screening task while being assisted by an adaptable diagnostic aid. Furthermore, it examined how both groups operated and trusted this automated system. 30 experts (certified screeners) and 31 novices (students) had to indicate whether a target item (either a knife or a gun) was present in a series of X-ray images of cabin baggage. Half of the participants could choose between three different support levels of the diagnostic aid (DA): (1) no support, (2) a cue indicating the presence of a potential target without locating it, or (3) a cue indicating the presence of a potential target by surrounding it with a red frame. As expected, experts achieved higher detection performance (d'), were more self-confident and felt more competent in achieving the task than novices. Furthermore, experts experienced less time pressure and fatigue. Although both groups used the DA in a comparable way (in terms of support level used and frequency of level switches), results showed a performance increase for novices working with the DA compared to novices without support. This benefit of DA was not observed for experts. Interestingly, despite no difference in perceived trust ratings, experts were more compliant (i.e., following DA recommendations when it indicated the presence of a target) and reliant (i.e., following DA recommendations when it indicated the absence of a target) than novices. Altogether, the results of the present study suggested that novices benefited more from a DA than experts. Furthermore, compliance and reliance on DA seemed to depend on expertise with the task. Since experts should be better at assessing the reliability of the DA than novices, they may have used the DA as 'back-up' to confirm their decisions based on expertise (confirmatory function), while novices may have used it as a guide to base their decisions on (support function). Finally, trust towards a DA was associated with the degree to which participants found the DA useful.