WO2020089654A1 - Améliorations dans ou se rapportant à un système pour un opérateur - Google Patents

Améliorations dans ou se rapportant à un système pour un opérateur Download PDF

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Publication number
WO2020089654A1
WO2020089654A1 PCT/GB2019/053113 GB2019053113W WO2020089654A1 WO 2020089654 A1 WO2020089654 A1 WO 2020089654A1 GB 2019053113 W GB2019053113 W GB 2019053113W WO 2020089654 A1 WO2020089654 A1 WO 2020089654A1
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WO
WIPO (PCT)
Prior art keywords
interest
image
parameter
entity
dataset
Prior art date
Application number
PCT/GB2019/053113
Other languages
English (en)
Inventor
Tristram Piers Benedict RILEY-SMITH
Original Assignee
XPCI Technology Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by XPCI Technology Limited filed Critical XPCI Technology Limited
Publication of WO2020089654A1 publication Critical patent/WO2020089654A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30112—Baggage; Luggage; Suitcase

Definitions

  • the present invention provides an improvement in or relating to a system for detecting the presence of an object of interest such as contraband, or an illicit threat item within a piece of baggage and notifying an operator such as a Security Officer (or an operator of security detection equipment) of its concealed presence, for instance in a piece of baggage.
  • a system and method can be provided for notifying the operator of any potential contraband or illicit or threatening items using X-ray Phase Contrast Imaging (XPCI) and Deep Learning models.
  • XPCI X-ray Phase Contrast Imaging
  • a system for notifying an operator of the presence of an object of interest within an entity comprising, an image scanner configured to image the entity containing at least one object to provide an image dataset of the entity and the object(s) contained therein, wherein the entity and object(s) contained therein comprise a plurality of parameters; a storage location configured to maintain a dataset comprising a plurality of signature profiles of objects of interest and a set of threshold values indicative of acceptable levels of correlation with each signature of an object of interest, wherein the signature profile comprises a plurality of parameters; a decision-making module configured to receive the dataset from the image scanner and correlate the image dataset of the object(s) within the entity with the plurality of signature profiles of objects of interest, wherein the decision-making module is further configured to determine whether the correlation between at least one parameter of the scanned image and at least one parameter of any one of the signatures of objects of interest exceeds the corresponding threshold value; and an output module associated with the decision-making module configured to provide a notification to the operator;
  • Examples of an object of interest may be one or more of the following: explosives, narcotics or a pharmaceutically relevant drug, contraband items such as ivory items or elephant tusks or a tumour within the human body, or a biopsy of human cells to detect suspicious cancerous or pre-cancerous cells.
  • the entity may be a physical entity.
  • Examples of an entity may be one or more of the following; a piece of baggage, a goods container such as a shipping container or an aircraft container, a human or animal body or a drug formulation.
  • the system could display the image or related data in such a way as to locate and highlight the presence of the suspected object(s) within the entity.
  • the threshold values may be adjusted to accommodate real world circumstances that might change or impact workflows. For example, the threshold values can be reduced or lowered to detect more items if a high-profile person is scheduled to go through a security checkpoint. This might be appropriate because the risk of a missed object of interest may be greater than the inconvenience of a higher than usual number of false positives, i.e. detections of possible object of interest that turn out to be benign.
  • threshold values being fine-tuned may be on the basis of the geographic location of the system i.e. to accommodate and detect certain objects or items at the borders of specific source-countries (e.g. elephant ivory from African countries) or countries where demand for illicit goods is high (e.g. China re ivory).
  • specific source-countries e.g. elephant ivory from African countries
  • countries where demand for illicit goods is high e.g. China re ivory
  • the image dataset can be derived from one or more of the following: Raman spectra, Gamma Ray or X-ray detection.
  • Raman Spectra is a detection technique that can be used for finding volatile liquids such as hydrogen peroxide hidden inside a drinks bottle such as Lucozade ® because volatile liquids that could be used in an Improvised Explosive Device can be difficult to distinguish from less dangerous counterparts.
  • Raman Spectroscopy is a detection technique that can be used for finding volatile liquids such as hydrogen peroxide hidden inside a drinks bottle such as Lucozade ® because volatile liquids that could be used in an Improvised Explosive Device can be difficult to distinguish from less dangerous counterparts.
  • Gamma-Ray Detectors can be deployed to search for dirty bombs / radioactive material being smuggled through ports, but it is not always easy to distinguish genuine threats from the signal noise.
  • the input data comes in the form of one or more images is generated by an X-ray scanner, with advances in technology allowing for more information to be extracted (for instance through X-Ray Phase Contrast Imaging).
  • the signature profile of objects of interest can be based on one or more of the following parameters: micro-structure and/or texture, shape, intensity, contrast, colour of the item.
  • the micro-structure and/or texture of a material can provide distinctive features that are associated with an explosive or an illicit item. It is an advantage of the system of the present invention to be able to detect these distinctive features of an object of interest or item because these distinctive textures are not readily recognisable to the human eye.
  • the one or more parameters of the signature profile of objects of interest may be compared with a plurality of different thresholds to form data comprising a plurality of elements. As a result, each element (e.g.
  • the system may then combine a plurality of elements from within the data to make an overall determination of the presence or absence of a threat item.
  • the presence and/or absence of an object of interest may be based on the correlation between a plurality of parameters of the scanned image and the signature profile of an object of interest exceeding the threshold.
  • the presence and/or absence of an object of interest may be based on the correlation between 1 , 2, 3, 4, 5, 10, 20, 50, 100, 1000 or more than 1000 parameters of the scanned image and the signature profile of an object of interest exceeding the threshold.
  • the decision-making module can informed by a Deep Learning Model that has been trained to identify tell-tale signatures that are difficult or almost impossible for the human eye to detect, or which can be missed through fatigue-induced errors as the attention span of humans begins to diminish after a relatively short period of time.
  • the decision-making module further comprises a feedback mechanism which adds image data, threshold correlation and outcomes to the storage location.
  • the feedback may be checked by a human operator to enhance the DNN threat library. This ensures that the system is adapted for active or continuous learning from real life security processes. This can lead to regular updates to the library of signature profiles of the object of interest informed, for instance, by instances of False Positives, improving the effectiveness of the system to disambiguate objects of interest to those that are of no interest.
  • the feedback mechanism enables the decision-making module to improve its performance in identifying True Positives. It can be envisaged that individual systems feeding back these lessons learned to a central repository allowing a universal release of updated signature profiles to all systems, ensuring the benefits of Active Learning are shared globally.
  • the system may further comprise a processing module associated with the x-ray scanner, wherein the processing module may be configured to process the x-ray image of the object to remove background noise.
  • the X-ray image may further be processed to remove background noise prior to inputting the image into the DNN model.
  • Figure 1 provides a schematic demonstration of a system for detecting an object of interest at a security checkpoint according to the present invention.
  • FIG. 1 there is provided a schematic showing a system of the present invention for notifying an operator of the presence of an object of interest within an entity such as a piece of baggage.
  • the system comprises inputs from a scanner or detector at a security checkpoint configured to gather data (for instance imaging a piece of baggage); a storage facility, which can be a virtual library, configured to maintain a dataset comprising a plurality of signature profiles of objects of interest and a set of threshold values indicative of acceptable levels of correlation with each signature of the object of interest.
  • a decision-making module which may also be referred to as a Virtual Assistant, may be configured to receive the dataset from the image scanner or detector and correlate the image dataset of the object(s) within the entity with the plurality of signature profiles of the object of interest, where the decision-making module may be further configured to determine whether the correlation between at least one parameter of any one of the signatures of the object of interest exceeds the corresponding threshold values for at least one parameter of that object of interest signature profile.
  • the decision-making module will typically be algorithms that interrogate one or more Deep Learning Models for instance, based on a Deep Neural Network or DNN trained externally to learn to identify tell-tale signatures.
  • An output module i.e.
  • a user interface associated with the decision-making module may be configured to provide a notification to the operator; where the notification is either one of the following: notification type A indicating the suspected presence of an object of interest based on the threshold values of at least one parameter of the signature profile of the object of interest being exceeded, or notification type B indicating an absence of an object of interest based on the probability of at least one parameter of the object being below the threshold value of at least one parameter of the signature profile of the object of interest.
  • Notification type A and/or B can be a visual and/or an audible notification, which may be used to alert the operator of the object of interest.
  • An output could also take the form of an automated instruction to the security scanner or detector to divert the bag (or other entities being scanned) into a channel for secondary screening.
  • the decision-making module also referred to as the Virtual Assistant, may be a computer-implemented system connected to an image scanner such as an X-ray scanner that uses the input of distinctive textures derived from the image e.g. XPCI images filtered through DNN Models based on objects or targets of interest e.g. explosives/ivory.
  • the DNN model can be used to process the received image and determine whether the object is an object of interest or not based on the probability that at least one parameter of the object exceeds the pre-determ ined threshold values of at least one parameter of the signature profile of the object of interest.
  • the DNN model is able to flag up in real-time the suspected presence of contraband and/or illicit items to personnel operating at security checkpoints.
  • the system of the present invention may be able to accommodate a“library” of DNN Models of target items, capable of being updated to reflect improvements in machine-learning and imaging. For instance, advances in x-ray technology include Computed Tomography, 3-D imaging, Multi-View Imaging and X-ray Phase Contrast Imaging.
  • the system may enable an operator to set one or more thresholds value for a signature profile of the object of interest or at least one parameter thereof, with the ability for the operator to also adjust the threshold values, in order to reflect changes of risks or the object of interest profile for instance, it might be acceptable to set the output probability to 0.25 for explosives when scanning bags going onto the flight of a very very important person (WIP) who is the target of assassination, or the same for elephant ivory at a place and time when intelligence suggests this is being smuggled out of the airport.
  • WIP very very important person
  • a feedback mechanism can be provided with the current system in order to support active training of the decision-making module i.e. the DNN model.
  • a feedback mechanism can be used to add image data, threshold correlation and outcomes to the storage location, or any other additional information deemed useful by an operator. This ensures that the system is adapted for continuous learning from real life processed images.
  • An example as to when a feedback mechanism is required can be when the decision-making module determines a false positive i.e. where the decision-making module wrongly suspects the presence of an object of interest.
  • the feedback can be submitted to a Deep Learning facility for retraining the DNN Models and upgrades are fed back to the library ensuring continuous improvement.
  • the feedback mechanism helps the decision-making module to continually “learn” in order to reduce or eliminate any false positives.
  • This feedback can ultimately be transferred to a universal Active Learning facility, leading to the performance of all systems to be improved through the release of revised signature profiles.
  • a double-blind trial can be carried out, as an example, in which the combination of x-ray images and DNN models can be used to detect explosive in X-ray images.
  • Table 1 show that the DNN model receiving XPCI images is able to process the images and provide a 100% accuracy rate in identifying explosive found in baggage.
  • Table 1 provide results of a trial for detecting explosives in X-ray images.
  • the system of the present invention may be intended to speed up the process of identifying an object of interest within an entity and to notify the operator.
  • airport security personnel took on average 6.56 seconds to review an x-ray image whereas the Virtual Assistant took 1.475 seconds.
  • the statistics are even more significant if it is recognised that airport reviewers examined one image per format whereas the Virtual Assistant can examine an x-ray image with multiple formats.
  • Active Training of the DNN model it can be anticipated that there would be fewer and fewer False Positives, significantly reducing delays caused by secondary scanning.
  • the combined application of XPCI and Deep Learning is intended to eliminate the need for electronic items to be removed from hand-luggage, which makes a small, positive impact on the speed of an individual’s journey through the system, but in aggregate makes a substantial impact when there are on average about 8 million air-passenger journeys a day (over 3 billion a year).

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  • Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)

Abstract

Cette invention concerne un système pour notifier un opérateur de la présence d'un objet d'intérêt dans une entité, le système comprenant : un dispositif de balayage d'image configuré pour imager l'entité ; un emplacement de stockage configuré pour maintenir un ensemble de données comprenant une pluralité de profils de signature d'objets d'intérêt ; un module de prise de décision configuré pour recevoir des données provenant du dispositif de balayage d'image et les corréler avec des données stockées dans l'emplacement de stockage pour déterminer si la corrélation entre au moins un paramètre de l'image scannée et au moins un paramètre de l'une quelconque des signatures d'objets d'intérêt dépasse une valeur seuil correspondante ; et un module de sortie associé au module de prise de décision configuré pour fournir une notification à l'opérateur.
PCT/GB2019/053113 2018-11-01 2019-11-01 Améliorations dans ou se rapportant à un système pour un opérateur WO2020089654A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB1817875.6 2018-11-01
GBGB1817875.6A GB201817875D0 (en) 2018-11-01 2018-11-01 Improvements in or relating to a system for an operator

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WO2020089654A1 true WO2020089654A1 (fr) 2020-05-07

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008040119A1 (fr) * 2006-10-02 2008-04-10 Optosecurity Inc. Plateau permettant d'évaluer si un article représente une menace au niveau d'un point de contrôle de sécurité
US7856081B2 (en) * 2003-09-15 2010-12-21 Rapiscan Systems, Inc. Methods and systems for rapid detection of concealed objects using fluorescence
US20120037811A1 (en) * 2007-09-11 2012-02-16 Kansas State University Research Foundation Remote substance detection

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7856081B2 (en) * 2003-09-15 2010-12-21 Rapiscan Systems, Inc. Methods and systems for rapid detection of concealed objects using fluorescence
WO2008040119A1 (fr) * 2006-10-02 2008-04-10 Optosecurity Inc. Plateau permettant d'évaluer si un article représente une menace au niveau d'un point de contrôle de sécurité
US20120037811A1 (en) * 2007-09-11 2012-02-16 Kansas State University Research Foundation Remote substance detection

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