US 12,033,312 B2
Systems and methods for determining defects in physical objects
Rachel Kohler, Fort Worth, TX (US); Darrell R. Krueger, Lecompton, KS (US); Kevin Lawhon, Cleburne, TX (US); and Garrett Smitley, Fort Worth, TX (US)
Assigned to BNSF Railway Company, Fort Worth, TX (US)
Filed by BNSF Railway Company, Fort Worth, TX (US)
Filed on Mar. 10, 2023, as Appl. No. 18/181,981.
Application 18/181,981 is a continuation of application No. 17/194,954, filed on Mar. 8, 2021, granted, now 11,620,743.
Application 17/194,954 is a continuation of application No. 16/196,990, filed on Nov. 20, 2018, granted, now 10,984,521, issued on Apr. 20, 2021.
Prior Publication US 2023/0326007 A1, Oct. 12, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06T 7/0004 (2013.01) [G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06T 7/11 (2017.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/20132 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A system for determining defects in physical objects, comprising:
an image capturing system, including a sub-frame, a beam, one or more sensors, a lighting system, a data system, and one or more controllers; and
a defect detector module, including:
an image collection engine configured to collect one or more images of one or more physical objects from the image capturing system;
a classification engine configured to classify one or more features from the images into one or more classifications;
a defect detector engine configured to analyze the one or more classifications to identify one or more defects; and
a reporting engine configured to generate a report indicating whether the physical object is defective based on the defect detector engine analysis;
wherein the classification engine analyzes the images via one or more machine learning algorithms; and
wherein the one or more machine learning algorithms output the one or more classifications that describe the one or more features of the one or more images within a level of certainty.