Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 1026-1033.doi: 10.13229/j.cnki.jdxbgxb20200058

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User interface components detection algorithm based on improved YOLOv3

Yuan-ning LIU1,2(),Di WU1,2,Xiao-dong ZHU1,2(),Qi-xian ZHANG1,3,Shuang-shuang LI1,2,Shu-jun GUO1,2,Chao WANG1,3   

  1. 1.Key Laboratory of Symbolic Computation and Knowledge Engineering，Ministry of Education，Jilin University，Changchun 130012，China
    2.College of Computer Science and Technology，Jilin University，Changchun 130012，China
    3.College of Software，Jilin University，Changchun 130012，China
  • Received:2020-01-23 Online:2021-05-01 Published:2021-05-07
  • Contact: Xiao-dong ZHU E-mail:lyn@jlu.edu.cn;zhuxd@jlu.edu.cn

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Abstract:

When the traditional detection methods are used to identify User Interface （UI） components， there is no way to categorize these components. To solve this problem in order to provide guidance for designers to reconstruct complex UI screenshot examples， this paper proposes an improved method based on improved YOLOv3 for UI components detection. First， the feature extraction network uses tightly connected network （DenseNet） to make full use of the extracted features. Second， channel attention mechanism and spatial attention mechanism are added to the dense layers and transition layers of the feature extraction network， the weighted feature is used to replace the original feature for the later feature fusion. Third， the feature pyramid network is constructed to complete the components detection task in four dimensions. Finally， the focalloss is used as the classification loss function. The data set consists of a large number of real Android application interface screenshots and XML files. The experimental results on the collected data set show that the recall rate of the method is 91.97% and the mAP is 48.21%， which has better performance than the traditional detection methods.

Key words: computer application, components detection, attention mechanism, Focalloss

CLC Number: 

  • TP391.4

Fig.1

Structure of channel attention mechanism"

Fig.2

Structure of spatial attention mechanism"

Fig.3

DenseBlock connection method"

Fig.4

DenseNet structure of three DenseBlocks"

Fig.5

Structure of the feature extraction network"

Table 1

Description information and samples of 14 UI components"

组件类别描述样例
ImageView用来在界面上显示图片。
TextView用来在界面上显示文本，是一些组件的父类，如：Button, EditText。
ImageButton以图片形式显示的按钮，可以添加点击事件。
CheckBox复选框可以选择多个项目的按钮。
EditText用来显示文本，文本内容是可编辑的。
RadioButton单选按钮Button的一种，只能选择一项。
Button可以显示文本，并且可以添加点击事件。
ProgressBar用于向用户显示某耗时操作的完成进度。
CompoundButton选中、未选中状态。
SeekBar允许用户拖动滑块改变相应值。
Switch通过点击改变开关状态。
RatingBar通常用于向用户显示等级。
ToggleButton状态开关按钮由Switch派生出来的。
Spinner供用户选择的列表框。

Fig.6

UI screenshot and TextView componentinformation in its XML file"

Table 2

Confusion matrix for classification results"

标签为正标签为负
预测为正TPFP
预测为负FNTN

Fig.7

IoU calculation method"

Table 3

Parameter settings in Focalloss"

αγPrecisionRecallmAP
0.2510.25980.66950.3776
0.5010.28870.69340.4147
0.7510.29570.70230.4289
0.2520.25840.64590.3755
0.5020.27700.76080.4251
0.7520.32080.71870.4480
0.2530.25540.67150.3754
0.5030.27010.69260.4226
0.7530.28790.71440.4484
0.7540.28300.72900.4344
0.7550.27720.72510.4273

Table 4

Results of different UI componentsdetection methods"

检测方法输入尺寸PrecisionRecallmAP
REMAUI(OCR+CV)原始大小0.10550.1359-
YOLOv2(合成UI数据集)416×4160.31250.7083-
YOLOv2(真实UI数据集)416×4160.22130.57730.4157
YOLOv3416×4160.17040.60310.4231
本文方法416×4160.23150.91970.4821

Fig.8

Result of detection"

Fig.9

Wrong detection result"

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