US 12,406,352 B1
Cable damage detection by machine vision
Matthew Bronars, Atlanta, GA (US); Tianxiang Su, Arlington, MA (US); Suraj Kiran Raman, Cambridge, MA (US); and Zhandos Ombayev, Sugar Land, TX (US)
Assigned to SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
Appl. No. 18/994,255
Filed by SCHLUMBERGER TECHNOLOGY CORPORATION, Sugar Land, TX (US)
PCT Filed Jul. 14, 2023, PCT No. PCT/US2023/027707
§ 371(c)(1), (2) Date Jan. 14, 2025,
PCT Pub. No. WO2024/015545, PCT Pub. Date Jan. 18, 2024.
Claims priority of provisional application 63/368,412, filed on Jul. 14, 2022.
Int. Cl. G06T 7/00 (2017.01); G06V 10/26 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01)
CPC G06T 7/0004 (2013.01) [G06V 10/273 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30108 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method for identifying cable damage using machine vision, comprising:
providing a camera directed toward a cable that is winding upon, or unwinding from, a cable spool;
capturing a plurality of frames of images of the cable by the camera;
cropping the frames by a region-of-interest (“ROI”) extractor to remove portions of the frames that do not include the cable;
processing the cropped frames using a machine-learning model, wherein the machine-learning model is trained using images of known cable damage as inputs; and
classifying each cropped frame as including damage or not including damage.