US 11,670,079 B1
Technologies for using image data analysis to assess and classify hail damage
Marigona Bokshi-Drotar, McKinney, TX (US); Jing Wan, Allen, TX (US); Sandra Kane, Garland, TX (US); and Yuntao Li, Champaign, IL (US)
Assigned to State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed by State Farm Mutual Automobile Insurance Company, Bloomington, IL (US)
Filed on Mar. 11, 2021, as Appl. No. 17/199,203.
Application 17/199,203 is a continuation of application No. 16/175,126, filed on Oct. 30, 2018, granted, now 10,977,490.
Int. Cl. G06T 7/00 (2017.01); G06V 20/10 (2022.01); G06Q 50/16 (2012.01); G06Q 40/08 (2012.01); G06V 10/82 (2022.01); G06V 10/56 (2022.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/54 (2022.01); G06V 20/17 (2022.01)
CPC G06V 20/176 (2022.01) [G06Q 40/08 (2013.01); G06Q 50/16 (2013.01); G06T 7/0002 (2013.01); G06V 10/25 (2022.01); G06V 10/26 (2022.01); G06V 10/54 (2022.01); G06V 10/56 (2022.01); G06V 10/82 (2022.01); G06T 2207/20084 (2013.01); G06V 20/17 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method of analyzing image data to automatically assess hail damage to a property, the method comprising:
accessing digital image data depicting a roof of the property;
segmenting, by a processor, the digital image data into a set of digital images depicting portions of the roof;
identifying, by the processor and using a convolutional neural network (CNN), regions of potential hail damage depicted in the set of digital images;
identifying, by the processor, features indicative of the potential hail damage and illustrated within the respective regions based at least in part on a numerical proximity of aspect ratios of the features to one, wherein identifying the features comprises:
determining sections of a digital image from the set of digital images, the sections including a first section within a region of potential hail damage and a second section outside of the region of potential hail damage; and
identifying a texture feature within the sections by performing a Gray-Level Co-Occurrence Matrices (GLCM) analysis on the sections to output a set of statistical properties comprising one or more of contrast, entropy, energy, or homogeneity for the first section and the second section; and
generating, by the processor, using a classification model, and based on the features, an output indicating a presence of hail damage associated with the roof, wherein the output is used to automatically determine an estimated damage amount to the roof of the property.