US 12,067,644 B2
Computer vision systems and methods for object detection with reinforcement learning
Maneesh Kumar Singh, Lawrenceville, NJ (US); and Sina Ditzel, Leuven (BE)
Assigned to Insurance Services Office, Inc., Jersey City, NJ (US)
Filed by Insurance Services Office, Inc., Jersey City, NJ (US)
Filed on Dec. 16, 2020, as Appl. No. 17/123,197.
Claims priority of provisional application 62/948,532, filed on Dec. 16, 2019.
Prior Publication US 2021/0182533 A1, Jun. 17, 2021
Int. Cl. G06T 11/00 (2006.01); G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06T 11/20 (2006.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 20/64 (2022.01)
CPC G06T 11/00 (2013.01) [G06F 18/2431 (2023.01); G06N 20/00 (2019.01); G06T 11/20 (2013.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 20/64 (2022.01); G06T 2210/12 (2013.01)] 22 Claims
OG exemplary drawing
 
1. A computer vision system for object detection with reinforcement learning, comprising:
a memory storing at least one image; and
a processor in communication with the memory, the processor:
setting a plurality of reinforcement learning agent parameters;
retrieving the at least one image from memory;
detecting a target object in the at least one image based on the reinforcement learning agent parameters;
determining a bounding box for the detected target object;
displaying the bounding box on the image;
performing reinforcement learning on a portion of the image appearing within the bounding box; and
when the reinforcement learning agent receives a positive terminal reward, performing at least one of: (1) evaluating whether an optimal number of rewards for the reinforcement learning agent can be improved; or (2) determining a better fitting bounding box.