Baggage Inspection Research Articles

Research on Security Model of Airport Luggage Remote Office System Based on Zero Trust

With the continuous development of modern information society, remote office equipment itself has many benefits and is widely respected by the public, but at the same time, it also brings many network security issues. In order to help the airport luggage remote office system achieve stability and solve security issues encountered during luggage inspection, an analysis of existing system security risks based on the zero trust concept is conducted, including factors such as data leakage, insufficient monitoring, and incomplete identity authentication. Combined with the characteristics of the luggage system and remote office technology, the security model is ensured to be sufficiently reliable, providing some reference for airlines preparing to implement zero trust airport luggage remote office.

Optimalisasi Peranpetugas Aviation Security dalam Menja min Keamanan dan Keselamatan Penerbangan di Bandar Udara Sultan Muhammad Salahuddin Bima

In maintaining airport security, each airport has a security officer, namely Aviation Security (AVSEC). AVSEC are airport security personnel who are required to have responsibility for ensuring flight security who are required to have a license, the role of Aviation Security (AVSEC) officers is very important in security and safety. This study aims to determine whether the role of Aviation Security (AVSEC) officers in carrying out their responsibilities for security is in accordance with the SOP, and what problems occur when Aviation Security (AVSEC) officers are carrying out their duties. This study uses qualitative methods, data collection techniques in this study are interviews, observation, and documentation. Data analysis techniques using data reduction, data presentation, and data conclusions/verification. The results of this study indicate that the role of Aviation Security (AVSEC) officers has carried out their duties properly in accordance with applicable SOPs and always prioritizes 3S + 1C for passenger comfort. Obstacles during passenger inspection caused an error in the engine so that the inspection was not optimal and inspection of passenger luggage was carried out manually by Aviation Security (AVSEC) officers. ,

In-depth analysis of automated baggage inspection using simulated X-ray images of 3D models

In-depth analysis of automated baggage inspection using simulated X-ray images of 3D models

How to Reduce the Number of Inspectors: Operation Mode and Modeling Simulation of Centralized Image Recognition in Railway Passenger Security Inspection

Security inspection is an essential step for passengers entering railway stations in China. Under the existing security inspection operation mode, each X-ray machine needs an image inspector to recognize the X-ray image, which poses the issue of resource optimization for inspectors. Based on the networking of security inspection X-ray machines, an innovative centralized image recognition (CIR) operation mode for railway security inspections is proposed, and it is worth noting that network and device failures are risk factors for this mode. The method for assigning image recognition personnel is designed to reduce labor force needed for security inspection. Using a combination of agent-based modeling method and computer simulation technology, the four elements of passengers, security inspection X-ray machines, X-ray image inspectors, and secondary baggage inspection personnel are modeled, and CIR indicators to study the effectiveness of the operation are designed. Based on the real departing passenger data of the Zhangjiakou section of the Beijing Zhangjiakou Line in China, simulation is conducted in two scenarios: large passenger flow and small passenger flow. The research is conducted from three aspects: operational efficiency, travel experience, and system complexity. Experiments have shown that, with a slight increase in passenger waiting time, the average number of X-ray image inspectors is reduced by more than 70% compared with the existing operation mode. The best pick-up location for passengers is related to passenger flow. The proposed model can analyze the effectiveness of CIR operation mode and provide decisive support for the application of CIR operation mode.

An automated detection model of threat objects for X-Ray baggage inspection based on modified encoder-decoder model

ABSTRACT Modern security faces challenges in detecting unauthorized and potentially harmful items in luggage, despite X-ray baggage scanning frameworks and research on efficiently screening highly disguised items. In response to this gap, a groundbreaking Modified Encoder-Decoder-based model has been introduced. This innovative model takes X-ray scan images as input and generates distinct feature representations for both suspicious and non-suspicious baggage materials. A key focus of the model is to address the denoising challenge inherent in X-ray images which reduces the models efficiency. This is achieved through the implementation of a Poisson Noise Reduction method during the preprocessing stage. Following preprocessing, the model effectively segments the non-threat image, identifying potential threats from the denoised input. The model showcases superior performance, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) and low Mean Squared Error (MSE) values, outperforming existing filtering techniques. Rigorous testing on publicly available SIXray and GDXray datasets validates the effectiveness of the proposed methodology. Performance metrics for the SIXray dataset, including mAP, IoU, and DC values of 97.32%, 73.14%, and 85.12%, respectively, underscore the model’s efficacy. Notably, the framework attains an impressive accuracy of 99.17% on the SIXray dataset, affirming its robustness in addressing contemporary security challenges.

Toward Dual-View X-Ray Baggage Inspection: A Large-Scale Benchmark and Adaptive Hierarchical Cross Refinement for Prohibited Item Discovery

Toward Dual-View X-Ray Baggage Inspection: A Large-Scale Benchmark and Adaptive Hierarchical Cross Refinement for Prohibited Item Discovery

Development of prototype backscatter X-ray security scanner for luggage inspection

In the Republic of Korea, for the purpose of security screening at ports and airports, the present system for inspecting small cargo/luggage relies on transmission X-ray imaging, with which discernment of organic and powder-form materials in images is difficult. Backscatter X-ray imaging techniques, contrastingly, are sensitive to organic materials (i.e., low-Z elements) due to a higher probability of Compton scattering relative to other photon interactions. Such techniques, therefore, have the potential to be used in security screening systems for the detection of organic compounds such as drugs and explosives. In the present study, a prototype backscatter X-ray security scanner for luggage screening was developed, and its performance was evaluated at various tube voltages. It was found that the isolation contrast, which indicates the minimum discernible thickness of a plastic panel when the background material of the image is steel, was approximately 1 mm at the tube voltage of 120 kVp. Moreover, amorphously shaped contraband articles randomly hidden inside the luggage were clearly visible at tube voltages ranging from 80 to 160 kVp. The developed backscatter X-ray security scanner is expected to provide improved detection efficiency for thin objects and/or illicit organic materials compared to a transmission-based X-ray imaging system.

Enhancing baggage inspection through computer vision analysis of x-ray images

Enhancing baggage inspection through computer vision analysis of x-ray images

Enhanced detonators detection in X-ray baggage inspection by image manipulation and deep convolutional neural networks

Detecting detonators is a challenging task because they can be easily mis-classified as being a harmless organic mass, especially in high baggage throughput scenarios. Of particular interest is the focus on automated security X-ray analysis for detonators detection. The complex security scenarios require increasingly advanced combinations of computer-assisted vision. We propose an extensive set of experiments to evaluate the ability of Convolutional Neural Network (CNN) models to detect detonators, when the quality of the input images has been altered through manipulation. We leverage recent advances in the field of wavelet transforms and established CNN architectures—as both of these can be used for object detection. Various methods of image manipulation are used and further, the performance of detection is evaluated. Both raw X-ray images and manipulated images with the Contrast Limited Adaptive Histogram Equalization (CLAHE), wavelet transform-based methods and the mixed CLAHE RGB-wavelet method were analyzed. The results showed that a significant number of operations, such as: edges enhancements, altered color information or different frequency components provided by wavelet transforms, can be used to differentiate between almost similar features. It was found that the wavelet-based CNN achieved the higher detection performance. Overall, this performance illustrates the potential for a combined use of the manipulation methods and deep CNNs for airport security applications.

Detect occluded items in X-ray baggage inspection

X-ray baggage inspection automatically determines whether there are prohibited items in passenger luggage and has recently achieved good progress due to the development of deep convolutional neural networks (DCNNs). However, the performance of X-ray baggage inspection degrades with heavily occluded and cluttered baggage owing to the interaction of the imaged objects. Inspired by the idea that discriminant cues may still exist in multiscale perceptions, discrepancies among different classes, and associations among different tasks, we propose a novel approach that exploits pseudo semantic masks to enhance the discriminant ability of feature representations. Moreover, the features at neighboring scales interact to further explore context information. We verify our method on the publicly available SIXray, OPIXray, and HIXray datasets. The results show that our method outperforms other state-of-the-art X-ray baggage inspection methods by 1.02% and 0.85% in terms of mean average precision (mAP).

Peran Petugas Aviation Security Dalam Pelayanan Keamanan Penerbangan di Bandar Udara Internasional Tjilik Riwut Palangkaraya

In maintaining airport security, each airport has a security officer, namely Aviation Security (AVSEC). AVSEC are airport security personnel who are required to have responsibility for ensuring flight security who are required to have a license, the role of Aviation Security (AVSEC) officers is very important in security and safety. This study aims to determine whether the role of Aviation Security (AVSEC) officers in carrying out their responsibilities for security is in accordance with the SOP, and what problems occur when Aviation Security (AVSEC) officers are carrying out their duties. This study uses qualitative methods, data collection techniques in this study are interviews, observation, and documentation. Data analysis techniques using data reduction, data presentation, and data conclusions/verification. The results of this study indicate that the role of Aviation Security (AVSEC) officers has carried out their duties properly in accordance with applicable SOPs and always prioritizes 3S + 1C for passenger comfort. Obstacles during passenger inspection caused an error in the engine so that the inspection was not optimal and inspection of passenger luggage was carried out manually by Aviation Security (AVSEC) officers.
 Keywords: Aviation Security, The Role of AVSEC, Security, Safety, Flight Convenience.

Material-Aware Path Aggregation Network and Shape Decoupled SIoU for X-ray Contraband Detection

X-ray contraband detection plays an important role in the field of public safety. To solve the multi-scale and obscuration problem in X-ray contraband detection, we propose a material-aware path aggregation network to detect and classify contraband in X-ray baggage images. Based on YoloX, our network integrates two new modules: multi-scale smoothed atrous convolution (SCA) and material-aware coordinate attention modules (MCA). In SAC, an improved receptive field-enhanced network structure is proposed by combining smoothed atrous convolution, using separate shared convolution, with a parallel branching structure, which allows for the acquisition of multi-scale receptive fields while reducing grid effects. In the MCA, we incorporate a spatial coordinate separation material perception module with a coordinated attention mechanism. A material perception module can extract the material information features in X and Y dimensions, respectively, which alleviates the obscuring problem by focusing on the distinctive material characteristics. Finally, we design the shape-decoupled SIoU loss function (SD-SIoU) for the shape characteristics of the X-ray contraband. The category decoupling module and the long–short side decoupling module are integrated to the shape loss. It can effectively balance the effect of the long–short side. We evaluate our approach on the public X-ray contraband SIXray and OPIXray datasets, and the results show that our approach is competitive with other X-ray baggage inspection approaches.

Analisis Penerapan Prosedur Penanganan Barang Berbahaya di Security Check Point (SCP) 1 Bandar Udara Internasional Raja Haji Fisabilillah Tanjungpinang

The implementation of procedures in dealing with dangerous goods in accordance with the existing rules in Security Check Point (SCP) 1 is one of the first steps in improving security at the airport, considering the increasingly diverse ways to commit crimes found at the airport related to the inspection of passengers and luggage. The purpose of this study is to analyze the implementation of hazardous goods handling procedures at Security Check Point (SCP) 1 and to find out the constraints of Aviation Security officers in handling dangerous goods at Security Check Point (SCP) 1 Raja Haji Fisabilillah Tanjungpinang International Airport.
 This study uses a descriptive qualitative approach with data collection techniques using participant observations, structured interviews, and documentation collected from related sources and when the author goes down the field.
 The results of this study show that the implementation of hazardous goods handling procedures at Security Check Point (SCP) 1 has been implemented in accordance with the rules or Standard Operating Procedures by Aviation Security personnel of Raja Haji Fisabilillah International Airport. This application is not only done when there are instructions from the x-ray operator but can also be applied if it looks suspicious passenger movements. In addition, there are also obstacles, namely differences of opinion with passengers who are arrogant and do not understand the regulations regarding luggage inspection which belongs to the category of dangerous goods or prohibited items and facilities that are suddenly error or not primed when used or run during the inspection time.
 Keywords : Aviation Security, Security Check Point (SCP) 1, application of procedures for handling dangerous goods.

Analisis Penerapan Prosedur Penanganan B3 di Bandar Udara Internasional Raja Haji Fisabilillah Tanjungpinang Riau

The implementation of procedures in dealing with dangerous goods in accordance with the existing rules in Security Check Point (SCP) 1 is one of the first steps in improving security at the airport, considering the increasingly diverse ways to commit crimes found at the airport related to the inspection of passengers and luggage. The purpose of this study is to analyze the implementation of hazardous goods handling procedures at Security Check Point (SCP) 1 and to find out the constraints of Aviation Security officers in handling dangerous goods at Security Check Point (SCP) 1 Raja Haji Fisabilillah Tanjungpinang International Airport. This study uses a descriptive qualitative approach with data collection techniques using participant observations, structured interviews, and documentation collected from related sources and when the author goes down the field.The results of this study show that the implementation of hazardous goods handling procedures at Security Check Point (SCP) 1 has been implemented in accordance with the rules or Standard Operating Procedures by Aviation Security personnel of Raja Haji Fisabilillah International Airport. This application is not only done when there are instructions from the x-ray operator but can also be applied if it looks suspicious passenger movements. In addition, there are also obstacles, namely differences of opinion with passengers who are arrogant and do not understand the regulations regarding luggage inspection which belongs to the category of dangerous goods or prohibited items and facilities that are suddenly error or not primed when used or run during the inspection time.
 Keywords : Aviation Security, Security Check Point (SCP) 1, application of procedures for handling dangerous goods.

Automated Detection of Threat Materials in X-Ray Baggage Inspection Systems (XBISs)

Manual inspection of baggage X-ray images for the detection of threat materials such as explosives is a tedious and stressful task. We have presented a convolutional neural network (CNN)-based approach for automated detection of threat materials in X-ray images. A deep convolutional generative adversarial network (DCGAN)-based data synthesized model is used for the generation of datasets containing gray images (DXBD1) and pseudo-colored images (DXBD2). A deep CNN-based model, named dual X-ray Baggage (DXB), is proposed for automated classification of threat materials in baggage X-ray images. The proposed model consists of seven layers comprising two convolution layers and five fully connected layers. With this model, accuracies of 98% for gray images with F1 score of 0.97 and 99% for pseudo-colored images with F1 score of 0.99 have been achieved. The proposed model requires the classification time of about 12 s and 6 s, respectively, for gray and pseudo-colored images indicating its suitability for real-time monitoring.

Analisis Kondisi Peralatan Unit Kerja Aviation Security Dalam Menunjang Keamanan Dan Keselamatan Penerbangan Di Security Check Point 1 Dan 2 Bandar Udara Abdulrachman Saleh Malang

The condition of avsec work unit equipment located in SCP 1 and 2 is very important for flight safety and security. Equipment that is in good condition and suitable for use according to the SOP applied will assist Avsec officers in carrying out their duties in SCP 1 and 2 as flight security and safety guards. The purpose of this study is to find out and qualitatively analyze the condition of Avsec work unit equipment in SCP 1 and 2 and the application of SOPs to the feasibility of Avsec equipment and the impact of equipment that has been damaged or errord to aviation safety and security at Abdulrahcman Saleh Malang Airport. This research uses qualitative research methods. Data sources come from primary and secondary data. Data collection techniques used are observation, interviews and documentation. The results showed that the condition of Avsec work unit equipment in the SCP 1 and 2 areas was good and not good. Abdulrachman Saleh Malang Airport still does not have Explonsive Detection System equipment used to detect explosives and Liquid Detectors that serve as liquid detection. Malang Airport has implemented sops on the feasibility of equipment by performing the correct operation, equipment maintenance and facility reporting. Damage or errors experienced by equipment have an impact on the inspection process is not optimal and the inspection of passengers' luggage is done manually by officers so that it can threaten the safety and security of the flight.

Towards More Efficient Security Inspection via Deep Learning: A Task-Driven X-ray Image Cropping Scheme.

X-ray imaging machines are widely used in border control checkpoints or public transportation, for luggage scanning and inspection. Recent advances in deep learning enabled automatic object detection of X-ray imaging results to largely reduce labor costs. Compared to tasks on natural images, object detection for X-ray inspection are typically more challenging, due to the varied sizes and aspect ratios of X-ray images, random locations of the small target objects within the redundant background region, etc. In practice, we show that directly applying off-the-shelf deep learning-based detection algorithms for X-ray imagery can be highly time-consuming and ineffective. To this end, we propose a Task-Driven Cropping scheme, dubbed TDC, for improving the deep image detection algorithms towards efficient and effective luggage inspection via X-ray images. Instead of processing the whole X-ray images for object detection, we propose a two-stage strategy, which first adaptively crops X-ray images and only preserves the task-related regions, i.e., the luggage regions for security inspection. A task-specific deep feature extractor is used to rapidly identify the importance of each X-ray image pixel. Only the regions that are useful and related to the detection tasks are kept and passed to the follow-up deep detector. The varied-scale X-ray images are thus reduced to the same size and aspect ratio, which enables a more efficient deep detection pipeline. Besides, to benchmark the effectiveness of X-ray image detection algorithms, we propose a novel dataset for X-ray image detection, dubbed SIXray-D, based on the popular SIXray dataset. In SIXray-D, we provide the complete and more accurate annotations of both object classes and bounding boxes, which enables model training for supervised X-ray detection methods. Our results show that our proposed TDC algorithm can effectively boost popular detection algorithms, by achieving better detection mAPs or reducing the run time.

A MCNP simulation for a new dual-energy dual-beam X-ray inspection method using multi-angle compton scattering to determine the effective atomic number of explosives

This study suggests an improved X-ray inspection method to determine the effective atomic number to identify explosives during luggage inspection with high resolution of the effective atomic number (Zeff). To obtain high resolution in Zeff, the suggested inspection method uses the ratio of the total and Compton scattering attenuation coefficients between two different energies for decomposing the attenuation coefficients of the Rayleigh scattering and photoelectric absorption that are highly dependent on the atomic number. To acquire the total and Compton scattering coefficient ratio of two different energies, dual-energy X-ray beams and four detectors for the photons transmitted and scattered in three different angles were introduced. MCNP6 simulations were carried out for several materials with X-ray source spectra from a tungsten anode generator to show the effectiveness of the method. The estimated Zeff values with the line beam assumption showed good agreement with the reference values for explosive substances within a relative error of 3% while the additional simulation considering beam width showed increased errors that are acceptable for beam widths less than 1.5 mm.

A Strategy-Based Formal Approach for Fog Systems Analysis

Fog systems are a new emergent technology having a wide range of architectures and pronounced needs making their design complex. Consequently, the design of fog systems is crucial, including service portability and interoperability between the various elements of a system being the most essential aspects of fog computing. This article presents a fog system cross-layer architecture as a first step of such a design to provide a graphical and conceptual description. Then, a BiAgents* (Bigraphical Agents) formal model is defined to provide a rigorous description of physical, virtual, and behavioural aspects of Fog systems. Besides, this formalisation is implemented and executed under a Maude strategy system. The proposed approach is illustrated through a case study: an airport terminal Luggage Inspection System (LIS) while checking the correctness of its relevant properties: the portability of data and their interoperability. The integration of the Maude strategies in the rewriting of Fog system states made it possible to guide the execution of the model and its analysis.

A Study on Cascade R-CNN-Based Dangerous Goods Detection Using X-Ray營mage

X-ray inspection equipment is divided into small baggage inspection equipment and large cargo inspection equipment. In the case of inspection using X-ray scanning equipment, it is possible to identify the contents of goods, unauthorized transport, or hidden goods in real-time by-passing cargo through X-rays without opening it. In this paper, we propose a system for detecting dangerous objects in X-ray images using the Cascade Region-based Convolutional Neural Network (Cascade R-CNN) model, and the data used for learning consists of dangerous goods, storage media, firearms, and knives. In addition, to minimize the overfitting problem caused by the lack of data to be used for artificial intelligence (AI) training, data samples are increased by using the CP (copy-paste) algorithm on the existing data. It also solves the data labeling problem by mixing supervised and semi-supervised learning. The four comparative models to be used in this study are Faster Region-based Convolutional Neural Networks Residual2 Network-101 (Faster R-CNN_Res2Net-101) supervised learning, Cascade R-CNN_Res2Net-101_supervised learning, Cascade Region-based Convolutional Neural Networks Composite Backbone Network V2 (CBNetV2) Network-101 (Cascade R-CNN_ CBNetV2Net-101)_supervised learning, and Cascade R-CNN_ CBNetV2-101_semi-supervised learning which are then compared and evaluated. As a result of comparing the performance of the four models in this paper, in case of Cascade R-CNN_CBNetV2-101_ semi-supervised learning, Average Precision (AP) (Intersection over Union (IoU) = 0.5): 0.7%, AP (IoU = 0.75): 1.0% than supervised learning, Recall: 0.8% higher.