Published on Sat Apr 25 2020

Detecting Electric Devices in 3D Images of Bags

Anthony Bagnall, Paul Southam, James Large, Richard Harvey

The aviation and transport security industries face the challenge of screening high volumes of baggage for threats and contraband. Traditional 2D x-ray images are often difficult to examine due to the fact that they are tightly packed. Major airports are introducing 3D Computed Tomography (CT) baggage scanning. We investigate whether we can automate the process of detecting electric devices in these 3D images of

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Abstract

The aviation and transport security industries face the challenge of screening high volumes of baggage for threats and contraband in the minimum time possible. Automation and semi-automation of this procedure offers the potential to increase security by detecting more threats and improve the customer experience by speeding up the process. Traditional 2D x-ray images are often extremely difficult to examine due to the fact that they are tightly packed and contain a wide variety of cluttered and occluded objects. Because of these limitations, major airports are introducing 3D x-ray Computed Tomography (CT) baggage scanning. We investigate whether we can automate the process of detecting electric devices in these 3D images of luggage. Detecting electrical devices is of particular concern as they can be used to conceal explosives. Given the massive volume of luggage that needs to be screened for this threat, the best way to automate the detection is to first filter whether a bag contains an electric device or not, and if it does, to identify the number of devices and their location. We present an algorithm, Unpack, Predict, eXtract, Repack (UXPR), which involves unpacking through segmenting the data at a range of scales using an algorithm known as the Sieve, predicting whether a segment is electrical or not based on the histogram of voxel intensities, then repacking the bag by ensembling the segments and predictions to identify the devices in bags. Through a range of experiments using data provided by ALERT (Awareness and Localization of Explosives-Related Threats) we show that this system can find a high proportion of devices with unsupervised segmentation if a similar device has been seen before, and shows promising results for detecting devices not seen at all based on the properties of its constituent parts.

Thu Jul 15 2021
Computer Vision
Unsupervised Anomaly Instance Segmentation for Baggage Threat Recognition
Unsupervised anomaly instance segmentation framework recognizes baggage threats, in X-ray scans, as anomalies. The framework is trained only once, and at the inference stage, it detects andextracts contraband items regardless of their scanner specifications.
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Mon Mar 25 2019
Computer Vision
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Automatic Threat Recognition (ATR) is now routine in aviation security industry. Current strategies use pre-defined threat material signatures in contrast to adaptability towards new and emerging threat signatures. Our proposed AATR performs well on both recognition and adaptation.
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The proposed framework has been extensively tested on four publicly available X-ray datasets. It outperforms the state-of-the-art frameworks in terms of mean average precision scores. It is the only framework that has been validated on combined grayscale and colored scans.
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Fri Mar 27 2020
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X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening. We aim to extend the automatic prohibited item detection from 2D X-ray imagery to volumetric 3D CT baggage security screening imagery. We take advantage of 3D Convolutional Neural
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Tue Apr 14 2020
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Cascaded Structure Tensor Framework for Robust Identification of Heavily Occluded Baggage Items from X-ray Scans
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Mon Dec 21 2020
Machine Learning
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Automatic prohibited object detection within 2D/3D X-ray Computed Tomography(CT) has been studied in literature to enhance aviation security screening. Deep Convolutional Neural Networks (CNN) have demonstrated superior performance in 2D x-ray imagery.
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