# Template-based Object Detection Using a Foundation Model Source: [Template-based Object Detection Using a Foundation Model]() Collection index: [llms.txt]() Data updated: 2026-03-24T05:00:03.763Z Authors: Valentin Braeutigam, Matthias Stock, Bernhard Egger Published: 2026-03-20T09:04:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on Dataset B (test)](): Object Detection; Dataset B (test); Precision: 99.72. - [Object Detection on Dataset A (test)](): Object Detection; Dataset A (test); Precision: 99.58. ## Original sources and implementation [Original paper]() [PDF]()