A framework for the automation of testing computer vision systems

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Vision systems, i.e., systems that enable the detection and tracking of objects in images, have gained substantial importance over the past decades. They are used in quality assurance applications, e.g., for finding surface defects in products during manufacturing, surveillance, but also automated driving, requiring reliable behavior. Interestingly, there is only little work on quality assurance and especially testing of vision systems in general. In this paper, we contribute to the area of testing vision software, and present a framework for the automated generation of tests for systems based on vision and image recognition with the focus on easy usage, uniform usability and expandability. The framework makes use of existing libraries for modifying the original images and to obtain similarities between the original and modified images. We show how such a framework can be used for testing a particular industrial application on identifying defects on riblet surfaces and present preliminary results from the image classification domain.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/ACM International Conference on Automation of Software Test, AST 2021
PublisherInstitute of Electrical and Electronics Engineers
Pages121-124
Number of pages4
ISBN (Electronic)9781665435673
DOIs
Publication statusPublished - May 2021
Externally publishedYes
Event2nd IEEE/ACM International Conference on Automation of Software Test, AST 2021 - Virtual, Online
Duration: 20 May 202121 May 2021

Publication series

NameProceedings - 2021 IEEE/ACM International Conference on Automation of Software Test, AST 2021

Conference

Conference2nd IEEE/ACM International Conference on Automation of Software Test, AST 2021
CityVirtual, Online
Period20/05/2121/05/21

Keywords

  • test case generation
  • testing image classifiers
  • testing vision software

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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