US 12,462,362 B2
Machine-learning framework for detecting defects or conditions of railcar systems
Mahbod Amouie, Atlanta, GA (US); Evan T. Gebhardt, Atlanta, GA (US); Gongli Duan, Snellville, GA (US); Myles Grayson Akin, Marietta, GA (US); Wei Liu, Manlius, NY (US); Tianchen Wang, Atlanta, GA (US); Mayuresh Manoj Sardesai, Atlanta, GA (US); and Ilya A. Lavrik, Atlanta, GA (US)
Assigned to Norfolk Southern Corporation, Atlanta, GA (US)
Filed by Norfolk Southern Corporation, Atlanta, GA (US)
Filed on May 10, 2023, as Appl. No. 18/315,176.
Application 18/315,176 is a continuation of application No. 17/962,971, filed on Oct. 10, 2022, granted, now 11,663,711.
Application 17/962,971 is a continuation of application No. 17/549,499, filed on Dec. 13, 2021, granted, now 11,468,551, issued on Oct. 11, 2022.
Application 17/549,499 is a continuation in part of application No. 16/938,102, filed on Jul. 24, 2020, granted, now 11,507,779, issued on Nov. 22, 2022.
Prior Publication US 2024/0070834 A1, Feb. 29, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); B61L 27/57 (2022.01); G06F 18/214 (2023.01)
CPC G06T 7/0002 (2013.01) [B61L 27/57 (2022.01); G06F 18/214 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/30232 (2013.01); G06T 2207/30248 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A computer-implemented method in which one or more processing devices perform operations comprising:
training a defect detection system to identify defects in moving railcars by at least:
obtaining a plurality of raw images depicting railcars;
generating a first plurality of secondary images using at least the plurality of raw images, wherein the first plurality of secondary images is generated by applying image augmenting operations to the plurality of raw images;
curating a first training dataset comprising images from the plurality of raw images and the first plurality of secondary images;
training a first machine-learning algorithm with the first training dataset; and
training a second machine-learning algorithm with a second training dataset comprising images from the plurality of raw images and a second plurality of secondary images;
capturing, via a field camera system, a plurality of field images of a moving railcar travelling along a railway, at least one of the plurality of field images showing the moving railcar from a first angle and at least one other of the plurality of field images showing the moving railcar from a second angle that is different from the first angle;
for each particular field image of the plurality of field images:
applying the first machine-learning algorithm to the particular field image to generate one or more first machine-learning algorithm outputs;
applying the second machine-learning algorithm to at least one of the one or more first machine-learning algorithm outputs to generate one or more second machine-learning algorithm outputs; and
generating, based at least in part on at least one of the one or more second machine-learning algorithm outputs, a defect determination corresponding to the particular field image;
detecting a defect of the moving railcar based at least in part on a plurality of defect determinations corresponding to the plurality of field images; and
in response to detecting the defect, performing one or more remediation operations comprising at least one of:
instructing a reduction of a travel speed of the moving railcar; or
instructing a re-routing of the moving railcar.