US 2026/0073490 A1 · MAKING AN AUGMENTED TRAINING DATA SET FOR TRAINING A MACHINE LEARNING DATA MODEL TO INTERPRET GROUND PENETRATING RADAR B-SCANS

Abstract

Methods of making a partially synthetic data set for training an AI to correct for noise in GPR B-scan data. The methods mimic noise observed in real B-scans. Original B-scans are augmented with a plurality of permutations of synthetic noise. Methods include processes for (i) mimicking observed blurring in B-scan images resulting from variations in translational speed of the detector, (ii) mimicking observed RF interference in B-scan images, and/or (iii) mimicking observed vertical shifts resulting from movement of the GPR unit due to uneven ground. Methods can further include processes for low-pass filtering B-scan images to remove inductive emission interference and artifacts resulting from dynamic range limitations.

Claims28

Dependent claims

A data augmentation method comprising the steps of:

encoding a B-scan image as an array variable having a width corresponding to a number of columns;
defining a first range corresponding to a width of the array subject to a noise-introducing operation;
selecting a value from the first range;
defining a second range as an integer range defining numbers of contiguous columns of the array, each number defining a size of a blur group;
selecting a plurality of blur group sizes from the second range, wherein the sum of the selected blur group sizes numerically corresponds to the value selected from the first range corresponding to a number of columns of the array subject to the noise-introducing operation;
mapping each selected blur group size to contiguous columns of the array;
applying the noise-introducing operation to each blur group;
and
encoding an augmented B-scan image.

6 more elements

A data augmentation method, the method comprising the steps of:

encoding a B-scan image as an array variable having a width corresponding to a number of columns;
defining a grouped range corresponding to allowed spacing values between grouped noise spikes;
defining a non-grouped range corresponding to allowed spacing values between non-grouped noise spikes;
defining a spike width range corresponding to allowed spike widths of noise spikes;
defining a spike intensity range corresponding allowed noise spike intensity values;
defining a spike intensity exponent range corresponding to allowed noise spike intensity exponent values;
defining a spike frequency content range corresponding to allowed frequency contents of noise spike frequency content values;
encoding a B-scan image as an array variable having a width corresponding to a total number of columns of the array;
designating a selected first group of columns of the array as comprising a first noise spike;
setting a spike width of the first spike to a value selected from the spike width range;
setting a spike intensity of the first spike to a value selected from the spike intensity range;
setting a spike intensity exponent of the first spike to a value selected from the spike intensity exponent range;
calculating noise values for each pixel in each column of the first group of columns;
adding the noise values to each pixel in each column of the first group of columns;
determining whether to group the first noise spike with a next noise spike based on a grouping probability function;
setting a distance between the first noise spike and the next noise spike to a value selected from the grouped range or the non-grouped range based on the outcome of the step of determining whether to group the first noise spike with the next noise spike;
selecting a next group of columns according to the distance set between the first noise spike and the next noise spike, and designating the next group of columns as comprising the next noise spike;
setting a spike width of the next spike to a value selected from the spike width range;
setting a spike intensity of the next spike to a value selected from the spike intensity range;
setting a spike intensity exponent of the next spike to a value selected from the spike intensity exponent range;
calculating noise values for each pixel in each column of the next group of columns;
and
adding the noise values to each pixel in each column of the next group of columns.

20 more elements

Dependencies are inferred from the claim text.

Making an augmented training data set for training a machine learning data model to interpret ground penetrating radar b-scans

Life of this patent

Making an augmented training data set for training a machine learning data model to interpret ground penetrating radar b-scans

Life of this patent

Abstract

Claims28