US 12,354,331 B2
Computer-implemented method for analyzing relevance of visual parameters for training a computer vision model
Christian Heinzemann, Ludwigsburg (DE); Christoph Gladisch, Renningen (DE); Matthias Woehrle, Bietigheim-Bissingen (DE); and Ulrich Seger, Leonberg-Warmbronn (DE)
Assigned to ROBERT BOSCH GMBH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Jan. 6, 2022, as Appl. No. 17/647,313.
Claims priority of application No. 10 2021 200 568.3 (DE), filed on Jan. 22, 2021.
Prior Publication US 2022/0237897 A1, Jul. 28, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/774 (2022.01); G06F 3/0484 (2022.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06T 7/35 (2017.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01)
CPC G06V 10/774 (2022.01) [G06F 3/0484 (2013.01); G06F 18/214 (2023.01); G06N 20/00 (2019.01); G06T 7/35 (2017.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01)] 18 Claims
OG exemplary drawing
 
1. A computer-implemented method for analysing relevance of visual parameters for training a computer vision model, the method comprising the following steps:
obtaining the computer vision model in an initial training state configured to perform a computer vision function of characterizing elements of observed scenes;
obtaining a visual data set and selecting from the visual data set a first subset of items of visual data, and providing a first subset of groundtruth data that corresponds to the first subset of visual data;
obtaining a first visual parameter set, with at least one visual parameter therein defining at least one visual state of at least one item in the first subset of visual data, wherein the at least one visual state is capable of affecting a regression result of the computer vision model;
applying the first subset of items of visual data to the computer vision model to obtain a plurality of predictions of elements of observed scenes in the first subset of items of visual data, wherein the predictions include at least one regression result of the at least one item in the first subset of visual data;
computing a corresponding plurality of performance scores of the first visual parameter set characterizing accuracy of the computer vision model when providing the predictions of the at least one regression result, using the first subset of groundtruth data;
performing a sensitivity analysis of the plurality of performance scores over a domain of the first visual parameter set;
generating a second subset of items of visual data and a second subset of groundtruth data that corresponds to the second subset of visual data according to the sensitivity analysis of the plurality of performance scores over the domain of the first visual parameter set.