XSeg：面向真实安检场景的大规模X射线违禁品分割基准
XSeg: A Large-scale X-ray Contraband Segmentation Benchmark For Real-World Security Screening
XSeg 提供了大规模像素级 X 射线违禁品分割基准，可用于评估和提升模型在安检场景下的实例分割与跨域泛化能力；APSAM 的 Energy-Aware Encoder 和 Adaptive Point Generator 为基于 SAM 的少点提示标注提供了可复用思路。
原文依据 we introduce XSeg. To the best of our knowledge, XSeg is the largest X-ray contraband segmentation dataset to date
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论X射线违禁品检测对公共安全至关重要。然而，当前方法主要依赖边界框标注，由于缺乏像素级监督和真实世界数据，限制了模型的泛化能力和性能。为解决这些局限，我们提出了XSeg。据我们所知，XSeg是迄今为止最大的X射线违禁品分割数据集，包含98,644张图像和295,932个实例掩码，并涵盖最新的30个常见违禁品类别。图像来源于公开数据集和我们合成的数据，并通过自定义的数据清洗流程过滤掉低质量样本。为实现准确高效的标注并减少人工标注工作量，我们提出了自适应点SAM（APSAM），这是一个基于分割一切模型（SAM）构建的专用掩码标注模型。我们通过引入能量感知编码器来增强掩码解码器的初始化，显著提升了对重叠物品的敏感性，从而解决了SAM跨域泛化能力差和检测堆叠物体能力有限的问题。此外，我们设计了自适应点生成器，使用户仅需一个粗略的点提示即可获得精确的掩码标签。在XSeg上的大量实验表明，APSAM具有优越的性能。
英文原文摘要 1313 字符
X-ray contraband detection is critical for public safety. However, current methods primarily rely on bounding box annotations, which limit model generalization and performance due to the lack of pixel-level supervision and real-world data. To address these limitations, we introduce XSeg. To the best of our knowledge, XSeg is the largest X-ray contraband segmentation dataset to date, including 98,644 images and 295,932 instance masks, and contains the latest 30 common contraband categories. The images are sourced from public datasets and our synthesized data, filtered through a custom data cleaning pipeline to remove low-quality samples. To enable accurate and efficient annotation and reduce manual labeling effort, we propose Adaptive Point SAM (APSAM), a specialized mask annotation model built upon the Segment Anything Model (SAM). We address SAM's poor cross-domain generalization and limited capability in detecting stacked objects by introducing an Energy-Aware Encoder that enhances the initialization of the mask decoder, significantly improving sensitivity to overlapping items. Additionally, we design an Adaptive Point Generator that allows users to obtain precise mask labels with only a single coarse point prompt. Extensive experiments on XSeg demonstrate the superior performance of APSAM.