English(EN) Localize Any Object in X-Ray Security Scans without Human Annotation

新框架可在X射线安检图像中无需人工标注即可实现物体定位

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

研究人员开发了LAO-X，一个旨在改进X射线安检图像中物体定位的自监督框架。该方法解决了X射线数据标注稀缺以及X射线图像与标准RGB图像相比具有独特特征的问题。LAO-X通过使用合成的图像-标注对和逐步增加物体复杂度的课程策略来调整Segment Anything Model 2 (SAM2)，在无需人工标注的情况下显著提高了定位精度。 AI

影响这项研究通过提高AI模型在X射线图像中识别物体而无需大量人工标注的能力，有望显著增强安检环境中的自动化威胁检测。

排序理由该集群包含一篇研究论文，详细介绍了用于X射线图像物体定位的新型自监督适应框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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本文如何被排名

Signal score: 7 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇研究论文，详细介绍了用于X射线图像物体定位的新型自监督适应框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Yaqi Cai, Mingxuan Liu, Lorenzo Vaquero, Ning Wang, Nan Pu, Feng Xue, Elisa Ricci, Nicu Sebe · 2026-10-07 04:00

无需人工标注即可在X射线安检图像中本地化任意物体

arXiv:2610.07326v1 Announce Type: new Abstract: Universal object localization in X-ray security inspection is critical for automated threat detection in safety-critical venues. However, unlike everyday RGB images that dominate web-scale visual data, X-ray scans exhibit distinct c…

本文如何被排名

报道来源 [1]

无需人工标注即可在X射线安检图像中本地化任意物体

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