Abstract
In this research, we explore the few-shot object detection application for identifying concealed objects in sub-terahertz security images, using fine-tuning based frameworks. To adapt these machine learning frameworks for the (sub-)terahertz domain, we propose an innovative pseudo-annotation method to augment the object detector by sourcing high-quality training samples from unlabeled images. This approach employs multiple one-class detectors coupled with a fine-grained classifier, trained on supporting thermal-infrared images, to prevent overfitting. Consequently, our approach enhances the model's ability to detect challenging objects (e.g., 3D-printed guns and ceramic knives) when few-shot training examples are available, especially in the real-world scenario where images of concealed dangerous items are scarce.