用深度学习自动识别IBM i系统界面组件，助力自动化测试。
AS400-DET: Detection using Deep Learning Model for IBM i (AS/400)
- 构建包含1050张截图的标注数据集，含日文界面
- 基于先进模型实现多类界面元素精准检测
- 适合需要自动化测试的遗留系统开发人员
本文提出一种针对IBM i系统（又称AS/400）的自动图形用户界面组件检测方法。我们构建了一个人工标注的数据集，包含1,050张系统屏幕截图，其中381张为日文界面。每张图像包含文本标签、文本框、选项、表格、说明、键盘和命令行等多种组件。基于当前最先进的深度学习模型，我们开发了检测系统，并在该数据集上评估了多种方法。实验结果表明，该数据集能有效支持从GUI屏幕中进行组件检测。通过自动识别屏幕组件，AS400-DET具备实现基于图形界面系统自动化测试的潜力。
原文摘要 · Abstract (English)
This paper proposes a method for automatic GUI component detection for the IBM i system (formerly and still more commonly known as AS/400). We introduce a human-annotated dataset consisting of 1,050 system screen images, in which 381 images are screenshots of IBM i system screens in Japanese. Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We then develop a detection system based on state-of-the-art deep learning models and evaluate different approaches using our dataset. The experimental results demonstrate the effectiveness of our dataset in constructing a system for component detection from GUI screens. By automatically detecting GUI components from the screen, AS400-DET has the potential to perform automated testing on systems that operate via GUI screens.
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