ArticleSensors (Basel, Switzerland)2023
FSVM: A Few-Shot Threat Detection Method for X-ray Security Images.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Automation of Detector Array Design for Baggage X-Ray Scanners.Sensors (Basel, Switzerland) · 2025Article
- Application of human-in-the-loop hybrid augmented intelligence approach in security inspection system.Frontiers in artificial intelligence · 2025Article
- Lightweight Detection Method for X-ray Security Inspection with Occlusion.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
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 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 models, FSVM has the highest performance and is more suitable for complex distributed datasets (e.g., X-ray parcels).
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Registered trials
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