In recent years, automatic detection of threats in X-ray baggage has become important in security inspection.However, the training of threat detectors often requires extensive, well-annotated images, which are hard to procure, especially for rare contraband items.In this paper, a few-shot SVM-constraint threat detection model, named FSVM is proposed, which aims at detecting unseen contraband items with only Decor a small number of labeled samples.Rather than simply finetuning the original model, FSVM embeds a derivable SVM layer to back-propagate the supervised decision information into the former layers.
A combined loss function utilizing SVM loss is also created as the additional constraint.We have evaluated FSVM on the public security baggage dataset SIXray, performing experiments on 10-shot and 30-shot samples under three class divisions.Experimental results show that compared with four common few-shot detection Shelving Unit models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g.
, X-ray parcels).