# US 2018/0322366 A1 · Neural network training image generation system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0322366 A1 - Document ID: US20180322366A1 - Status: Published application - Kind: A1 - Application number: 15/584,129 - Filed: 2017-05-02 - Published: 2018-11-08 - Earliest claimed: 2017-05-02 - Assignees: General Electric Company - Applicants: General Electric Company - Inventors: Ser Nam Lim; Arpit Jain; David Scott Diwinsky; Sravanthi Bondugula - Web page: https://patentgrep.com/patent/US20180322366A1 ## CPC - G06V 10/82: using neural networks - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06F 18/2155: {characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling} - G06F 18/28: Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries - G06T 7/0004: {Industrial image inspection} - G06T 7/0008: {checking presence/absence} - G06V 10/22: by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition - G06V 10/772: Determining representative reference patterns, e.g. averaging or distorting patterns; Generating dictionaries - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30156: Vehicle coating - G06T 2207/30164: Workpiece; Machine component - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A system that generates training images for neural networks includes one or more processors configured to receive input representing one or more selected areas in an image mask. The one or more processors are configured to form a labeled masked image by combining the image mask with an unlabeled image of equipment. The one or more processors also are configured to train an artificial neural network using the labeled masked image to one or more of automatically identify equipment damage appearing in one or more actual images of equipment and/or generate one or more training images for training another artificial neural network to automatically identify the equipment damage appearing in the one or more actual images of equipment. ## Claims ### Claim 1 A system comprising: one or more processors configured to receive input representing one or more selected areas in an image mask, the one or more processors configured to form a labeled masked image by combining the image mask with an unlabeled image of equipment, wherein the one or more processors are configured to train an artificial neural network using the labeled masked image to one or more of automatically identify equipment damage appearing in one or more actual images of equipment or generate one or more training images for training another artificial neural network to automatically identify the equipment damage appearing in the one or more actual images of equipment. ### Claim 2 (depends on 1) The system of claim 1, wherein the one or more processors are configured to receive the input representing locations of where artificial anomalies are to appear on the equipment in the labeled masked image. ### Claim 3 (depends on 1) The system of claim 1, wherein the equipment includes a turbine engine and the one or more selected areas indicate locations on the turbine engine where damage to a coating of the turbine engine is to appear in the labeled masked image. ### Claim 4 (depends on 1) The system of claim 1, wherein pixels of the labeled masked image are annotated with indications of objects represented by the pixels. ### Claim 5 (depends on 1) The system of claim 1, wherein pixels of the unlabeled image are not annotated with indications of objects represented by the pixels. ### Claim 6 (depends on 1) The system of claim 1, wherein the image mask is a binary mask including two different types of areas to appear in the labeled masked image. ### Claim 7 (depends on 6) The system of claim 6, wherein a first type of the types of areas to appear in the labeled masked image is an artificial appearance of damage to the equipment and a second type of the types of areas to appear in the labeled masked image is an unchanged portion of the unlabeled image. ### Claim 8 A method comprising: receiving input representing one or more selected areas in an image mask; forming a labeled masked image by combining the image mask with an unlabeled image of equipment; and training an artificial neural network using the labeled masked image to one or more of automatically identify equipment damage appearing in one or more actual images of equipment or generate one or more training images for training another artificial neural network to automatically identify the equipment damage appearing in the one or more actual images of equipment. ### Claim 9 (depends on 8) The method of claim 8, wherein the input that is received represents locations of where artificial anomalies are to appear on the equipment in the labeled masked image. ### Claim 10 (depends on 8) The method of claim 8, wherein the equipment includes a turbine engine and the one or more selected areas indicate locations on the turbine engine where damage to a coating of the turbine engine is to appear in the labeled masked image. ### Claim 11 (depends on 8) The method of claim 8, wherein pixels of the labeled masked image are annotated with indications of objects represented by the pixels. ### Claim 12 (depends on 8) The method of claim 8, wherein pixels of the unlabeled image are not annotated with indications of objects represented by the pixels. ### Claim 13 (depends on 8) The method of claim 8, wherein the image mask is a binary mask including two different types of areas to appear in the labeled masked image. ### Claim 14 (depends on 13) The method of claim 13, wherein a first type of the types of areas to appear in the labeled masked image is an artificial appearance of damage to the equipment and a second type of the types of areas to appear in the labeled masked image is an unchanged portion of the unlabeled image. ### Claim 15 A system comprising: one or more processors configured to receive an actual image of equipment, the actual image not including annotations of what object is represented by each pixel in the actual image, the one or more processors also configured to obtain an image mask, the image mask representing one or more selected areas where damage to the equipment is to appear, the one or more processors configured to generate a labeled masked image by combining the actual image with the image mask, wherein the labeled masked image includes annotations of what object is represented by plural pixels in the one or more selected areas from the image mask. ### Claim 16 (depends on 15) The system of claim 15, wherein the one or more processors are configured to train an artificial neural network using the labeled masked image to automatically identify equipment damage appearing in one or more additional images of equipment. ### Claim 17 (depends on 15) The system of claim 15, wherein the one or more processors are configured to generate one or more training images for training an artificial neural network to automatically identify equipment damage appearing in the one or more additional images of equipment. ### Claim 18 (depends on 15) The system of claim 15, wherein the equipment includes a turbine engine and the one or more selected areas indicate locations on the turbine engine where damage to a coating of the turbine engine is to appear in the labeled masked image. ### Claim 19 (depends on 15) The system of claim 15, wherein the image mask is a binary mask including two different types of areas to appear in the labeled masked image. ### Claim 20 (depends on 19) The system of claim 19, wherein a first type of the types of areas to appear in the labeled masked image is an artificial appearance of damage to the equipment and a second type of the types of areas to appear in the labeled masked image is an unchanged portion of the unlabeled image.