| CPC G06T 7/0004 (2013.01) [G06T 7/11 (2017.01); G06T 2200/24 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/30136 (2013.01)] | 20 Claims |
|
1. A method for training and/or using a semantic deep learning model to detect surface corrosion, comprising:
receiving, by a training dataset generation tool of an application executing on one or more computing devices, a training dataset that includes a plurality of images of infrastructure having at least some surface corrosion;
applying, by the training dataset generation tool, an unsupervised image segmentation algorithm to segment a first portion of the images of the training dataset;
prompting a user to manually label at least some of the segments of the first portion as corrosion segments to produce labeled images;
optimizing, by the training dataset generation tool, classification rules based on the labeled images of the first portion to produce a rule-based classifier;
applying, by the training dataset generation tool, the rule-based classifier to a second portion of the images of the training dataset to automatically segment and label at least some of the segments of the second portion as corrosion segments; and
using the labeled training dataset to train the semantic deep learning model to detect and segment corrosion in images of an input dataset.
|