The Boeing Company

Amir Afrasiabi

William David Kelsey

Nick Shadbeh Evans

Brian Dale Laughlin

Kwang Hee Lee

Young Suk Cho

Zachary R Smith

Ashutosh Mani

Hieu Tat Nguyen

Gabriel Burnett

Rosemary D Pham

Shabnam Zangeneh-Khamooshi

Jacob Pete McHenry

Chandler Chockalingam

Bhargavi Patel

Junghyun Oh

Rosemary Pham

Jacob Pete Mchenry

An apparatus for identifying objects receives a dataset comprising an image having first real image features; trains a neural network to recognize the first real image features in the received dataset; and performs a synthetic image augmentation to generate synthetic image features corresponding to the first real image features in the received dataset using the neural network. The synthetic image augmentation allows for improved training of a computer vision function for recognizing second real image features, corresponding to the synthetic image features, in a real-world environment.

Unpaired image-to-image translation using a generative adversarial network (GAN)

Techniques for adversarial attack avoidance for machine learning (ML) are disclosed. These techniques include receiving one or more images at a trained ML model and receiving attack data at the ML model. The techniques further include predicting an object depicted in the one or more images using the ML model, based on the one or more images, metadata relating to the one or more images, and the attack data. The ML model uses the metadata to prevent the attack data from changing a result of the predicting.

Adversarial attack detection and avoidance in computer vision

Certain aspects of the present disclosure provide techniques for autonomous image acquisition. This includes determining a plurality of two-dimensional image perspectives for a plurality of image capture devices, and comparing the plurality of two-dimensional image perspectives with a generated two-dimensional representation of a target object, where the two-dimensional representation is generated based on a three-dimensional model of the target object. This further includes automatically moving at least one of the plurality of image capture devices, based on the comparing, to increase a portion of the target object captured by the plurality of image capture devices.

Autonomous image acquisition systems for production and environment monitoring and event detection

A system for automated surface anomaly detection includes at least one processor which is configured to retrieve from non-volatile memory a 3D control image; capture a 3D target image depicting at least the portion of the exterior of a target object; generate 2D target planar images of the target object based on the 3D target image, using a first plurality of virtual cameras; generate 2D control planar images of the control object based on the 3D control image using a second plurality of virtual cameras, the 2D control planar images corresponding to the 2D target planar images of the target object; detect at least one difference between the 2D target planar images and the 2D control planar images; and generate and cause to be displayed an output image comprising a depiction of the target object with the at least one difference indicated.

System and method for automated surface anomaly detection

A computing device, a method and a computer program product are provided to generate realistic synthetic image data that is utilized to train, test and/or deploy a computer vision model, such as to recognize an object from an image, to identify a visual anomaly in the object from an image of the object or the like. In the context of a method, a plurality of computer generated three-dimensional (3D) models of an object are generated. The method also generates realistic synthetic image data representative of each of the plurality of computer generated 3D models. The realistic synthetic image data is generated by utilizing a first machine learning model trained with pairs of computer generated synthetic image data and real image data of the object. The method further trains, tests or deploys the computer vision model to recognize the object from an image based upon the realistic synthetic image data.

Computing device and method for generating realistic synthetic image data

A method, apparatus, system, and computer program product for training a gesture recognition machine learning model system. Temporal images for a set of gestures used for ground operations for an aircraft are identified by a computer system. Pixel variation data identifying movement on a per image basis from the temporal images is generated by the computer system. The temporal images and the pixel variation data form training data. A set of feature machine learning models is trained by the computer system to recognize features using the training data.

Human gesture recognition for autonomous aircraft operation

An image generation system is provided to: receive a 3D CAD (computer aided design) model comprising 3D model images of a target object; based on the 3D CAD model of the target object, generate augmented CAD models of the target object comprising data sets, each data set respectively corresponding to an associated one of a plurality of attribute classes, each data set comprising a plurality of 2D model images; input the data sets into a generative adversarial network comprising generators respectively corresponding to the plurality of attribute classes and discriminators respectively corresponding to the generators; generate synthetic photorealistic images of the target object using the generators, the synthetic photorealistic images including attributes in accordance with the data sets corresponding to the plurality of attribute classes; and output the synthetic photorealistic images of the target object.

Systems and methods for synthetic image generation

A human machine interface (HMI), a method of interacting with an HMI and a corresponding computer program product facilitate user interaction with an HMI. In a method, a face of a person attempting to interact with the HMI is detected and a gesture made by the person relative to the HMI is also detected. The method analyzes information regarding the face of the person that has been detected to determine whether the person is wearing personal protection equipment. In an instance in which the person is determined to not be wearing personal protection equipment, the method suspends a response to the gesture made by the person. However, in an instance in which the person is determined to be wearing personal protection equipment, the method analyzes the gesture made by the person relative to the HMI and converts the gesture to a corresponding command to the system associated with the HMI.

Human machine interface and associated method of interaction

An apparatus for inspecting a surface comprises a light source and a camera. When a light beam is generated by the light source, a centerline of the light beam is coincident with a line of sight of the camera when viewed in a direction, perpendicular to a first plane that contains one of the centerline of the light beam or the line of sight of the camera. The centerline of the light beam is parallel to the line of sight of the camera when viewed in a direction, perpendicular to a second plane that is perpendicular to the first plane and that contains the centerline of the light beam and the line of sight of the camera.

Apparatuses and methods for inspecting a surface

An image generation system is provided to: receive a 3D CAD (computer aided design) model comprising 3D model images of a target object; generate a graph data structure; based on the 3D CAD model of the target object and the graph data structure, generate a plurality of augmented CAD models of the target object comprising a plurality of data sets, each data set respectively corresponding to an associated one of a plurality of attribute classes, each data set comprising a plurality of 2D model images; input the plurality of data sets into a generative adversarial network; generate synthetic photorealistic images of the target object using the plurality of generators of the generative adversarial network, the synthetic photorealistic images including attributes in accordance with the plurality of data sets corresponding to the plurality of attribute classes; and output the synthetic photorealistic images of the target object.

Systems and methods for detecting surface anomalies are disclosed. For example, a computer-implemented method for detecting surface anomalies on an object comprises receiving measured electromagnetic radiation (EMR) profiles for the object, generating synthetic EMR profiles for the object based on the measured EMR profiles, determining whether the object contains a surface anomaly based on the measured EMR profiles and the synthetic EMR profiles, and indicating a surface anomaly to a user via a display when a surface anomaly is detected. In another example, a system comprises a computing device comprising non-transitory memory with computer-readable instructions for receiving unpaired image data of an object of two different image types, predicting missing image data to generate paired image data of the two different image types, and determining whether the object contains a surface anomaly based on the paired image data. The computing device comprises a processor configured to execute the computer-readable instructions.

Synthetic image generation for surface anomaly detection

A method for managing nonconformances in laminates. The method comprises recording, by a sensor system, layup information about a layup of layers on a workpiece platform, wherein the layup of layers forms a workpiece and recording inspection information about the laminate on an inspection platform, wherein the laminate is formed from curing the workpiece. An analyzer in a computer system identifies a laminate nonconformance in the laminate using the inspection information and a user input verifies the laminate nonconformance in the laminate is present. An artificial intelligence system is trained by the computer system using the layup information, the inspection information, and the user input verifying the laminate nonconformance.

Laminate nonconformance management system

A method, apparatus, and system for managing nonconformances in laminates. A nonconformance management system comprises a sensor system and an analyzer in a computer system. The sensor that records layup information about the layup of layers on a workpiece platform and records inspection information about the laminate located on an inspection platform, wherein the laminate is formed from curing the workpiece. The analyzer in the computer system identifies a laminate nonconformance in the laminate using the inspection information, generates nonconformance information about the laminate nonconformance, and displays the nonconformance information about the laminate nonconformance on the laminate using a display system for an augmented reality display.

A method and apparatus is provided to inspect coverage of a coating applied to a surface of a component. A coating color space value of a color of the coating is obtained based on a camera, a light source, and the surface, each value having an associated coating thickness applied to the surface. Images of the coating covered surface are obtained. Each image is processed by determining color space values of the image, determining whether the associated coating thickness of the color space values is within a specified tolerance of a required thickness of coating based on a comparison of the color space values to color space values associated with the required thickness of coating, and responsive to the associated thickness of coating of the color space values being outside of the specified tolerance, providing an indication that the surface shown in the image is outside of the specified tolerance.

Method and apparatus for coating thickness inspection of a surface and coating defects of the surface

According to various embodiments, a technique for automated aircraft paint application quality inspection is presented. The technique includes retrieving from electronic persistent storage a control image depicting at least a portion of aircraft paint application for the aircraft under quality inspection; capturing a process image depicting at least a portion of aircraft paint application on the aircraft under quality inspection; detecting a plurality of features in the control image and the process image; registering the control image with the process image; detecting at least one difference between the control image and the process image; generating an output image, where the output image includes a depiction of the aircraft under quality inspection with the at least one difference annotated; and causing the output image to be displayed.

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Seattle, WA, United States of America

Amir Afrasiabi