Systems and Methods for Detecting Threats and Contraband in Cargo

Patent metadata

Field	Value
Publication number	US-2020103548-A1
Application number	US-201916376971-A
Country	US
Kind code	A1
Filing date	Apr 5, 2019
Priority date	Feb 22, 2016
Publication date	Apr 2, 2020
Grant date	—

Abstract

The present specification discloses systems and methods for identifying and reporting contents of a tanker, container or vehicle. Programmatic tools are provided to assist an operator in analyzing contents of a tanker, container or vehicle. Manifest data is automatically imported into the system for each shipment, thereby helping security personnel to quickly determine container contents. In case of a mismatch between container contents shown by manifest data and the contents as ascertained from the scanning system, the container or vehicle may be withheld for further inspection.

First claim

1 - 16 . (canceled)

17 . A method for processing a radiographic image to identify whether at least one firearm is present in an object, the method comprising:
receiving the radiographic image of the object; applying a neural network to the radiographic image to minimize errors in an association of portions of the radiographic image with one or more predetermined features; and based on the association with minimized errors, generating a visual demarcation of an area in the radiographic image to identify a presence of the at least one firearm in the object.

18 . The method of claim 17 , wherein the neural network comprises a plurality of interconnected layers defined by variables associated with the one or more predetermined features, wherein the one or more predetermined features are indicative of a firearm.

19 . The method of claim 17 , further comprising detecting the at least one firearm in more than one physical orientation.

20 . The method of claim 17 , further comprising generating reference images wherein the reference images depict firearms in a plurality of physical orientations.

21 . The method of claim 20 , further comprising identifying the presence of the at least one firearm even if the at least one firearm does not exactly match a firearm in one of the reference images.

22 . The method of claim 17 , wherein the neural network is a deep belief network.

23 . The method of claim 17 , wherein the radiographic image comprises attenuation data.

24 . The method of claim 17 , further comprising building orientation invariant descriptors of features in radiographic images.

25 . The method of claim 17 , further comprising applying one or more classifier routines to the one or more features in the radiographic image.

26 . The method of claim 25 , wherein the one or more features in the radiographic image comprises at least one effective density, attenuation, geometric properties, size, area, or aspect ratio.

27 . A computer readable non-transitory medium comprising a plurality of executable programmatic instructions executed by a processor for implementing a process for processing a radiographic image to identify whether at least one firearm is present in an object, said plurality of executable programmatic instructions comprising:
programmatic instructions, stored in said computer readable non-transitory medium, for receiving the radiographic image of the object; programmatic instructions, stored in said computer readable non-transitory medium, for applying a neural network to the radiographic image to minimize errors in an association of portions of the radiographic image with one or more predetermined features; and programmatic instructions, stored in said computer readable non-transitory medium, for generating a visual demarcation of an area in the radiographic image to identify a presence of the at least one firearm in the object based on the association with minimized errors.

28 . The computer readable non-transitory medium of claim 27 , wherein the neural network comprises a plurality of interconnected layers defined by variables associated with the one or more predetermined features, wherein the one or more predetermined features are indicative of a firearm.

29 . The computer readable non-transitory medium of claim 27 , further comprising programmatic instructions, stored in said computer readable non-transitory medium, for detecting the at least one firearm in more than one physical orientation.

30 . The computer readable non-transitory medium of claim 27 , further comprising programmatic instructions, stored in said computer readable non-transitory medium, for generating reference images wherein the reference images depict firearms in a plurality of physical orientations.

31 . The computer readable non-transitory medium of claim 30 , further comprising programmatic instructions, stored in said computer readable non-transitory medium, for identifying the presence of the at least one firearm even if the at least one firearm does not exactly match a firearm in one of the reference images.

32 . The computer readable non-transitory medium of claim 27 , wherein the neural network is a deep belief network.

33 . The computer readable non-transitory medium of claim 27 , wherein the radiographic image comprises attenuation data.

34 . The computer readable non-transitory medium of claim 27 , further comprising programmatic instructions, stored in said computer readable non-transitory medium, for building orientation invariant descriptors of the one or more features in radiographic images.

35 . The computer readable non-transitory medium of claim 27 , further comprising programmatic instructions, stored in said computer readable non-transitory medium, for applying one or more classifier routines to the one or more features in the radiographic image.

36 . The computer readable non-transitory medium of claim 35 , wherein the one or more features in the received radiographic image comprises at least one effective density, attenuation, geometric properties, size, area, or aspect ratio.

Assignees

• Rapiscan Systems Inc

Inventors

• Yu Songyang
• Chittineni Rupa
• Mccarthy Mark J

Classifications

• G01F17/00

  Methods or apparatus for determining the capacity of containers or cavities, or the volume of solid bodies (measuring linear dimensions to determine volume G01B) · CPC title

• G06T2207/10116

  X-ray image · CPC title

• G01N2223/639

  material in a container · CPC title

• G01N2223/401

  image processing · CPC title

• G01F22/00

  Methods or apparatus for measuring volume of fluids or fluent solid material, not otherwise provided for · CPC title