CVHCLGSEAug 12, 2020

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

arXiv:2008.05132v2165 citations
AI Analysis

This addresses the problem of accurately detecting GUI elements for software engineering tasks like testing and code generation, representing an incremental improvement by combining existing techniques.

The paper tackles GUI element detection by conducting a large-scale empirical study of seven methods on over 50k GUI images and designing a new hybrid method that combines a GUI-specific old-fashioned approach with deep learning. Their evaluation on 25,000 GUI images shows significant advancement in state-of-the-art performance.

Detecting Graphical User Interface (GUI) elements in GUI images is a domain-specific object detection task. It supports many software engineering tasks, such as GUI animation and testing, GUI search and code generation. Existing studies for GUI element detection directly borrow the mature methods from computer vision (CV) domain, including old fashioned ones that rely on traditional image processing features (e.g., canny edge, contours), and deep learning models that learn to detect from large-scale GUI data. Unfortunately, these CV methods are not originally designed with the awareness of the unique characteristics of GUIs and GUI elements and the high localization accuracy of the GUI element detection task. We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods. This study not only sheds the light on the technical challenges to be addressed but also informs the design of new GUI element detection methods. We accordingly design a new GUI-specific old-fashioned method for non-text GUI element detection which adopts a novel top-down coarse-to-fine strategy, and incorporate it with the mature deep learning model for GUI text detection.Our evaluation on 25,000 GUI images shows that our method significantly advances the start-of-the-art performance in GUI element detection.

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