On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

Gaus, Yona Falinie A.; Bhowmik, Neelanjan; Breckon, Toby P.

doi:10.1109/hst47167.2019.9032917

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…Most studies in security applications aim at detection of threats. Some works focus on binary detection tasks to discriminate a specific class of objects, e.g., firearms/firearm components [73]- [75]. The works in [5], [63], [74]- [77] aim at recognizing multiple different threat categories, such as knives and guns, whereas the work in [78] aims at categorizing several object types, such as laptops and mobile phones, either as benign or anomalous.…”

Section: Securitymentioning

confidence: 99%

“…It achieves good performance with dense and small-scale objects, as the focal loss better addresses the problems caused by a major class imbalance between background and foreground classes. In the topic of X-ray image analysis, RetinaNet is used in [48], [73], [78] to detect defects in welding, anomalies in cluttered security imagery, and firearms in baggage security imagery, respectively.…”

Section: ) Deep Object Detection Architecturesmentioning

confidence: 99%

“…9-b) was used to segment anomalies in cluttered security imagery. Casting defects and firearms in baggage were segmented using Mask R-CNN in [4], [73].…”

Section: U-netmentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: Securitymentioning

confidence: 99%

Section: ) Deep Object Detection Architecturesmentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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“…Automatic object detection and recognition algorithms have been proposed and evaluated for baggage aviation security screening based on 2D X-ray images [1], [4], [6]. The use of CNN architectures and object detection frameworks boosts the performance with a high detection rate and a low false positive rate.…”

Section: B Baggage Security Screeningmentioning

confidence: 99%

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery

Wang

Bhowmik

Breckon

2020

2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)

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Automatic prohibited object detection within 2D/3D X-ray Computed Tomography (CT) has been studied in literature to enhance the aviation security screening at checkpoints. Deep Convolutional Neural Networks (CNN) have demonstrated superior performance in 2D X-ray imagery. However, there exists very limited proof how deep neural networks perform in materials detection within volumetric 3D CT baggage screening imagery. We attempt to close this gap by applying Deep Neural Networks in 3D contraband substance detection based on their material signatures. Specifically, we formulate it as a 3D semantic segmentation problem to identify material types for all voxels based on which contraband materials can be detected. To this end, we firstly investigate 3D CNN based semantic segmentation algorithms such as 3D U-Net and its variants. In contrast to the original dense representation form of volumetric 3D CT data, we propose to convert the CT volumes into sparse point clouds which allows the use of point cloud processing approaches such as PointNet++ towards more efficient processing. Experimental results on a publicly available dataset (NEU ATR) demonstrate the effectiveness of both 3D U-Net and PointNet++ in materials detection in 3D CT imagery for baggage security screening.

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“…Unlike the lower layers of CNN, the higher layers are finely tuned across the secondary problem domains with every related characteristic. By this, one can advance a prior CNN parametrizing quality of a currently trained network on a general class of object problem, as an initial point for optimization process towards the particular problem domain of limited class of object detection [4] (ex. images in X-ray).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation

Ponnusamy¹,

Marur²,

Dhanaskodi³

et al. 2021

RIA

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This work proposes deep learning neural network-based X-ray image classification. The X-ray baggage scanning machinery plays an essential role in the safeguard of customs, airports, and other systematically very important landmarks and infrastructures. The technology at present of baggage scanning machines is designed on X-ray attenuation. The detection of threatful objects is built on how different objects attenuate the X-ray beams going through them. In this paper, the deep convolutional neural network of YOLO is utilized in classifying baggage images. Real-time performance of the baggage image classification is an essential one for security scanning. There are many computationally intensive operations in the You Only Look Once (YOLO) architecture. The computational intensive operations are implemented in the Field Programmable Gate Array (FPGA) platform to optimize process delays. The critical issues involved in those implementations include data representation, inner products computation and implementation of activation function and resolving these issues will also be a significant task. The FPGA implementation results show that with less resource occupancy, the YOLO implementation provides maximum accuracy of 98.9% in classifying X-ray baggage images and identifying hazardous materials. This result proves that the proposed implementation is best suited for practical system deployments for real-time Baggage scanning.

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On the Use of Deep Learning for the Detection of Firearms in X-ray Baggage Security Imagery

Cited by 13 publications

References 18 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Multi-Class 3D Object Detection Within Volumetric 3D Computed Tomography Baggage Security Screening Imagery

Deep Learning-Based X-Ray Baggage Hazardous Object Detection – An FPGA Implementation

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