Clickable Object Detection Network for a Wide Range of Mobile Screen Resolutions

Boseon Kang, Minseok Jo, Chang Sung Jeong

Research output: Contribution to journalArticlepeer-review

Abstract

Recently, as the development cycle of applications has been shortened, it is important to develop rapid and accurate application testing technology. Since application testing requires a lot of cost, mobile GUI component detection technology using deep learning is essential to prevent the use of expensive human resources. In this paper, we shall propose a Clickable Object Detection Network (CODNet) for mobile component detection in a wide range of mobile screen resolutions. CODNet consists of three modules: feature extraction, deconvolution and prediction modules in order to provide performance improvement and scalability. Feature extraction module uses squeeze and excitation blocks to efficiently extract features by changing the ratio of the input image to 1:2 most close to that of mobile screen. Deconvolution module provides feature map of various sizes by upsampling feature map through top-down pathway and lateral connections. Prediction module selects an anchor size most suitable for the mobile environment using Anchor Transfer block among the set of anchor candidates obtained through the analysis of mobile dataset. Moreover, we shall show that our model achieves competitive performance in mean average precision on our dataset compared to the other models, and object detection performance is improved by building a new mobile screen dataset which consists of data collected from various resolutions and operating systems.

Original languageEnglish
Pages (from-to)115051-115060
Number of pages10
JournalIEEE Access
Volume10
DOIs
Publication statusPublished - 2022

Keywords

  • computer vision
  • deep neural networks
  • mobile screen
  • Object detection

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering

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