FSVM: A Few-Shot Threat Detection Method for X-ray Security Images

FSVM: A Few-Shot Threat Detection Method for X-ray Security Images

Blog Article

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 Mud Flap Kit 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 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 Jump Saddle 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 models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels).