Template-based Object Detection Using a Foundation Model
This work addresses the need for training-free object detection in specific domains like software testing, offering a practical solution for continuous integration in industries such as automotive, though it is incremental as it builds on existing foundation models.
The paper tackles the problem of object detection in scenarios with limited data variation and no training data, such as automated testing of graphical interfaces, by using segmentation foundation models combined with feature-based classification. The method achieves results nearly comparable to learning-based methods like YOLO, without requiring training.
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.