US 12,423,980 B2
Drone based automated yard check
Nicholas Dryer, North Barrington, IL (US); Zachery Bettis, Fort Worth, TX (US); Bryan Gabric, Weatherford, TX (US); Michael I. Ibanez, McAllen, TX (US); and Yasha Hajizeinalibiouki, Plano, TX (US)
Assigned to BNSF Railway Company, Fort Worth, TX (US)
Filed by BNSF Railway Company, Fort Worth, TX (US)
Filed on Jun. 28, 2024, as Appl. No. 18/758,673.
Application 18/758,673 is a continuation of application No. 18/340,151, filed on Jun. 23, 2023, granted, now 12,056,931.
Application 18/340,151 is a continuation of application No. 17/822,999, filed on Aug. 29, 2022, granted, now 11,688,169, issued on Jun. 27, 2023.
Prior Publication US 2024/0355122 A1, Oct. 24, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 20/50 (2022.01); G01S 19/45 (2010.01); G06Q 10/087 (2023.01); G06T 7/70 (2017.01); G06V 10/70 (2022.01); G06V 20/17 (2022.01); G06V 20/40 (2022.01); H04N 5/77 (2006.01); H04N 7/18 (2006.01)
CPC G06V 20/50 (2022.01) [G01S 19/45 (2013.01); G06Q 10/087 (2013.01); G06T 7/70 (2017.01); G06V 10/70 (2022.01); G06V 20/17 (2022.01); G06V 20/41 (2022.01); H04N 5/77 (2013.01); H04N 7/183 (2013.01); G06T 2207/10016 (2013.01); G06T 2207/10032 (2013.01); G06T 2207/30244 (2013.01); G06V 2201/10 (2022.01)] 20 Claims
OG exemplary drawing
 
1. An automated inventory control system, comprising:
an unmanned aerial vehicle configured to traverse an intermodal storage facility following a flight route;
a processor configured to detect at least one object in at least one image of a plurality of input images received from the unmanned aerial vehicle; and
a memory operably coupled to the processor and storing processor-readable code that, when executed by the processor, is configured to:
inspect each object detected in each image of the set of images;
determine a horizontal pixel location of a center of an object detected in the image;
determine a relative angle from the unmanned aerial vehicle to the object;
determine an object heading based on a heading of the unmanned aerial vehicle;
generate, for each object detection in each image of the set of images, a line that is based on the observation heading for each object detection and a location of the image capturing device at the time the image was captured; and
determine a location of the particular unique object by calculating a mean of all line intersections in a region of intersections for all observations of the object.