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<p>Recognising objects within graphical user interface (GUI) images presents unexpected challenges, particularly in cases of diverse objects and limited labelled data. In this paper, we enumerate the unique characteristics of GUI images from human-machine interface (HMI) screens and investigate several techniques for detecting appropriate objects present in them. We propose <Emphasis FontCategory="NonProportional">SAMatch</Emphasis>, a novel training-free matching-based approach utilising a frozen foundation model, <Emphasis FontCategory="NonProportional">SAM</Emphasis> for region proposal and a CNN-based model for deep template <Emphasis FontCategory="NonProportional">MATCH</Emphasis>ing. Through experimental evaluation, this paper compares approaches toward efficient processing of HMI screens using Computer Vision.</p>

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SAMatch: training-free object detection for HMI screens

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Kiruthika Kannan,
  • Vijay Jaisankar,
  • Akhil Pillai,
  • Rakesh Tripathi

摘要

Recognising objects within graphical user interface (GUI) images presents unexpected challenges, particularly in cases of diverse objects and limited labelled data. In this paper, we enumerate the unique characteristics of GUI images from human-machine interface (HMI) screens and investigate several techniques for detecting appropriate objects present in them. We propose SAMatch, a novel training-free matching-based approach utilising a frozen foundation model, SAM for region proposal and a CNN-based model for deep template MATCHing. Through experimental evaluation, this paper compares approaches toward efficient processing of HMI screens using Computer Vision.