用基础模型分割+特征分类，免训练检测图标
Template-based Object Detection Using a Foundation Model
- 利用分割基础模型生成区域，结合特征分类检测目标
- 在导航图图标检测任务中接近YOLO性能，无需训练
- 适合需频繁更换设计的自动化界面测试场景
当前主流目标检测方法依赖学习和训练数据，适用于外观变化大的场景。本文聚焦数据变化少的场景，提出无需训练数据生成和模型训练的检测方案，适用于软件开发中的图形界面自动测试，尤其适合持续集成测试。方法基于分割基础模型提取图像片段，结合简单特征分类器实现检测与分类。该方式在更换待检测对象或设计时无需重训或建数据集，显著节省时间和成本。我们在导航地图图标检测任务上评估该方法，结果显示其性能几乎达到基于学习的检测方法（如YOLO）水平，且无需训练。
原文摘要 · Abstract (English)
Most currently used object detection methods are learning-based, and can detect objects under varying appearances. Those models require training and a training dataset. We focus on use cases with less data variation, but the requirement of being free of generation of training data and training. Such a setup is for example desired in automatic testing of graphical interfaces during software development, especially for continuous integration testing. In our approach, we use segments from segmentation foundation models and combine them with a simple feature-based classification method. This saves time and cost when changing the object to be searched or its design, as nothing has to be retrained and no dataset has to be created. We evaluate our method on the task of detecting and classifying icons in navigation maps, which is used to simplify and automate the testing of user interfaces in automotive industry. Our methods achieve results almost on par with learning-based object detection methods like YOLO, without the need for training.
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