构建首个大规模X光违禁品分割数据集，提升安检模型精度与泛化能力。
XSeg: A Large-scale X-ray Contraband Segmentation Benchmark For Real-World Security Screening
- 基于SAM改进的APSAM模型，用单点提示实现高精度分割。
- 在98,644张图像上完成295,932个实例标注，覆盖30类违禁品。
- 适用于真实安检场景的细粒度检测，适合安全筛查与自动化系统开发。
X射线违禁品检测对公共安全至关重要。然而现有方法主要依赖边界框标注，因缺乏像素级监督和真实世界数据，限制了模型泛化能力与性能。为此，我们提出XSeg——目前已知最大的X射线违禁品分割基准数据集，包含98,644张图像和295,932个实例掩码，涵盖最新30类常见违禁品。图像来自公开数据集及合成数据，经自定义清洗流程去除低质样本。为实现高效精准标注，我们提出自适应点SAM（APSAM），在基础分割任意模型（SAM）基础上引入能量感知编码器，增强掩码解码器初始化，显著提升对重叠物品的敏感性；同时设计自适应点生成器，仅需单个粗略点提示即可获得精确掩码标签。在XSeg上的大量实验表明，APSAM表现优异。
原文摘要 · Abstract (English)
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.
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