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arXiv · 2606.15663

OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Model

Jiali Wen·Hongxia Gao·Litao Li·Yixin Chen·Kaijie Zhang·Qianyun Liu·Xiaoqin Wen
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Abstract

X-ray contraband detection is critical for security in large-scale logistics and transportation, yet conventional detectors struggle to adapt to emerging contraband types and lack fundamental visual understanding. Vision-language models (VLMs) offer strong generalization but are hindered by the scarcity of high-quality X-ray image-caption data. To bridge this critical gap, we present MMXray, a meticulously curated benchmark of 52,124 image-caption pairs spanning 28 fine-grained classes of X-ray contraband. To enrich MMXray with realistic occlusion patterns, we further introduce CleanDET, a dedicated synthesis dataset containing clean foreground contraband images from 28 categories and background images with diverse density levels, together with AnyContraSyn, a controllable synthesis method designed to operate on CleanDET. We also develop OnePipe, an extensible pipeline for systematic data curation. Built on MMXray, we propose OneFocus, a unified VLM that supports four core tasks: visual question answering, contraband localization, classification, and image understanding. OneFocus achieves state-of-the-art performance in X-ray contraband understanding and demonstrates robust cross-domain generalization, establishing a strong vision-language baseline for security screening.

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Jiali Wen, Hongxia Gao, Litao Li, Yixin Chen, Kaijie Zhang, Qianyun Liu, Xiaoqin Wen. 2026-06-14. OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Model. https://arxiv.org/abs/2606.15663

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