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公开(公告)号：US11468266B2
公开(公告)日：2022-10-11
申请号：US16586465
申请日：2019-09-27
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Stephen J. Raif , Philip A. Sallee , Jeffrey S. Klein , Steven A. Israel , Franklin Tanner , Shane A. Zabel , James Talamonti , Lisa A. Mccoy
Abstract: A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.
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公开(公告)号：US11373064B2
公开(公告)日：2022-06-28
申请号：US16518728
申请日：2019-07-22
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Shane A. Zabel
Abstract: Discussed herein are systems, devices, and methods for automatic target recognition based on a non-visible input image. A method can include providing, as input to a first machine learning (ML) model for object classification, pixel data of a non-visible image, the first ML model including an encoder from a second ML model, the second ML model trained to generate a visible image representation of an input non-visible image, and receiving, from the first ML model, data indicating one or more objects present in the non-visible image.
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公开(公告)号：US20210326645A1
公开(公告)日：2021-10-21
申请号：US17180111
申请日：2021-02-19
Applicant: Raytheon Company
Inventor： Robert F. Cromp , Jonathan Goldstein
Abstract: A computer accesses a plurality of image frames. The computer identifies, within the plurality of image frames, a plurality of vehicle front vehicle back detections. The computer pairs at least a subset of the plurality of vehicle back detections with vehicle front detections. A given vehicle back detection is paired with a given vehicle front detection based on camera angle relative to a predefined axis. The computer assigns, using each of a plurality of pools, a score to each vehicle front detection—vehicle back detection pair, each non-paired vehicle front detection, and each non-paired vehicle back detection. Each pool comprises a data structure representing a scoring mechanism and a set of detections. The computer assigns each detection to a pool that assigned a highest score to that detection. Upon determining that a given pool comprises at least n detections: the computer labels the given pool as representing a specific vehicle.
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公开(公告)号：US20210097344A1
公开(公告)日：2021-04-01
申请号：US16586465
申请日：2019-09-27
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Stephen J. Raif , Philip A. Sallee , Jeffrey S. Klein , Steven A. Israel , Franklin Tanner , Shane A. Zabel , James Talamonti , Lisa A. Mccoy
Abstract: A machine receives a large image having large image dimensions that exceed memory threshold dimensions. The large image includes metadata. The machine adjusts an orientation and a scaling of the large image based on the metadata. The machine divides the large image into a plurality of image tiles, each image tile having tile dimensions smaller than or equal to the memory threshold dimensions. The machine provides the plurality of image tiles to an artificial neural network. The machine identifies, using the artificial neural network, at least a portion of the target in at least one image tile. The machine identifies the target in the large image based on at least the portion of the target being identified in at least one image tile.
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公开(公告)号：US12039807B2
公开(公告)日：2024-07-16
申请号：US17385407
申请日：2021-07-26
Applicant: Raytheon Company
Inventor： Harrison Wong , Kirk E. Hansen , Philip A. Sallee , Drasko Sotirovski , Ronald F. Vega , Jonathan Goldstein
IPC: G07B15/06 , G01C5/00 , G01S7/4865 , G01S7/487 , G01S17/10 , G01S17/89 , G06F18/2413 , G06N3/08 , G08G1/017
CPC classification number: G07B15/06 , G01C5/00 , G01S7/4865 , G01S7/4876 , G01S17/10 , G01S17/89 , G06F18/24137 , G06N3/08 , G08G1/017 , G06V2201/08
Abstract: Systems, devices, methods, and computer-readable media for. A method can include receiving, from a laser scan device of a tolling station, a time series of distance measurements, determining, based on the time series of distance measurements, height measurements indicating a height of a vehicle from a surface of a road. generating, based on the height measurements, an image of the height measurements, and classifying, using the image as input to a convolutional neural network (CNN), the vehicle.
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Patent Agency Ranking
公开(公告)号：US11068747B2
公开(公告)日：2021-07-20
申请号：US16586480
申请日：2019-09-27
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Philip A. Sallee , James Mullen , Franklin Tanner
Abstract: A neural network apparatus includes processing circuitry and memory. The memory stores a plurality of images of a target. The processing circuitry is configured to: access, from the memory, a first image and an identification of a centroid pixel of the target within the first image; generate, based on a geometry of the target and the centroid pixel, a confidence map indicating, for each pixel in the first image, a confidence value that the pixel includes the target; train, using the plurality of images of the target, including the first image and the confidence map, an artificial neural network to identify the target in visual data; and provide an output representing the trained artificial neural network.
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公开(公告)号：US20210097345A1
公开(公告)日：2021-04-01
申请号：US16586480
申请日：2019-09-27
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Philip A. Sallee , James Mullen , Franklin Tanner
Abstract: A neural network apparatus includes processing circuitry and memory. The memory stores a plurality of images of a target. The processing circuitry is configured to: access, from the memory, a first image and an identification of a centroid pixel of the target within the first image; generate, based on a geometry of the target and the centroid pixel, a confidence map indicating, for each pixel in the first image, a confidence value that the pixel includes the target; train, using the plurality of images of the target, including the first image and the confidence map, an artificial neural network to identify the target in visual data; and provide an output representing the trained artificial neural network.
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公开(公告)号：US20210027113A1
公开(公告)日：2021-01-28
申请号：US16518728
申请日：2019-07-22
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Shane A. Zabel
IPC: G06K9/62
Abstract: Discussed herein are systems, devices, and methods for automatic target recognition based on a non-visible input image. A method can include providing, as input to a first machine learning (ML) model for object classification, pixel data of a non-visible image, the first ML model including an encoder from a second ML model, the second ML model trained to generate a visible image representation of an input non-visible image, and receiving, from the first ML model, data indicating one or more objects present in the non-visible image.
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公开(公告)号：US11676391B2
公开(公告)日：2023-06-13
申请号：US17180111
申请日：2021-02-19
Applicant: Raytheon Company
Inventor： Robert F. Cromp , Jonathan Goldstein
CPC classification number: G06F18/214 , G06N3/04 , G06T7/20 , G06T7/70 , G06V20/13 , G06T2207/30244 , G06T2207/30248
Abstract: A computer accesses a plurality of image frames. The computer identifies, within the plurality of image frames, a plurality of vehicle front vehicle back detections. The computer pairs at least a subset of the plurality of vehicle back detections with vehicle front detections. A given vehicle back detection is paired with a given vehicle front detection based on camera angle relative to a predefined axis. The computer assigns, using each of a plurality of pools, a score to each vehicle front detection—vehicle back detection pair, each non-paired vehicle front detection, and each non-paired vehicle back detection. Each pool comprises a data structure representing a scoring mechanism and a set of detections. The computer assigns each detection to a pool that assigned a highest score to that detection. Upon determining that a given pool comprises at least n detections: the computer labels the given pool as representing a specific vehicle.
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公开(公告)号：US11562184B2
公开(公告)日：2023-01-24
申请号：US17181581
申请日：2021-02-22
Applicant: Raytheon Company
Inventor： Jonathan Goldstein , Steven J. Shumadine , Christopher A. Eccles
Abstract: A computer obtains image frames. The computer identifies a chip within the image frames, the chip having a position and dimensions determined based on a lane width. Based on a speed and a length of a vehicle passing through a field of view of the camera, the computer selects a subset of the image frames. The computer takes, from each of the image frames in the subset, the identified chip for use as input to an artificial neural network (ANN). The computer individually provides each taken chip as input to the ANN to generate an ANN output. Based on a combination of the ANN outputs, the computer identifies a shape, a number of axles, and a number of segments of the vehicle. The computer provides a tuple representing the vehicle shape, the number of axles, and the number of segments.
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