PRISM-X: Progressive semi-supervised threat detection in X-ray scans with self-guided multimodal refinement

Abstract

X-ray baggage screening demands robust automated detection systems that can perform reliably under limited annotation. We introduce PRISM-X, a progressive semi-supervised framework that integrates pseudo-label generation, region-level multimodal grounding, and contrastive refinement into a unified detection pipeline. Evaluated on the SIXray and CLCXray benchmarks, PRISM-X consistently outperforms both vision-only and vision-language baselines. On SIXray, it achieves 66.0% mAP and 87.4% AP50, improving over the strongest vision-only method by +6.8% mAP and the leading vision-language model by +6.7% grounding accuracy. On CLCXray, PRISM-X reaches 45.7% mAP, 64.7% AP50, and 51.6% AP75, surpassing Cascade R-CNN by +3.3% mAP and GDINO by +3.4% AP75. These results demonstrate the effectiveness of PRISM-X in low-label, cluttered X-ray scenarios and its superiority over existing weakly and semi-supervised approaches.

keywords: Contrastive learning, Pseudo-labeling, Self-supervised learning, Semi-supervised learning, Vision-language models, X-ray baggage detection