针对安检X光图像中违禁品重叠难题，提出双层掩码解码新方法
Prohibited Items Segmentation via Occlusion-aware Bilayer Modeling
- 引入SAM模型弥补违禁品与自然物的视觉差异
- 设计显式建模遮挡关系的双层掩码解码器
- 构建两个带遮挡标注的大规模数据集，适用于安全检测场景
安检X光图像中违禁品实例分割是一项关键但极具挑战的任务，主要源于违禁品与自然物体间显著的外观差异，以及图像中物体间的严重重叠。为此，我们提出一种考虑遮挡的实例分割流程，以识别X光图像中的违禁品。为弥合表征差距，将通用分割模型SAM集成到流程中，利用其丰富的先验知识和零样本泛化能力。为解决物品重叠问题，设计了显式建模遮挡关系的遮挡感知双层掩码解码模块。为监督遮挡估计，我们在两个大规模X光图像分割数据集PIDray和PIXray上手动标注了违禁品的遮挡区域，并将其与原始信息重组为两个带遮挡标注的数据集PIDray-A和PIXray-A。在这些新数据集上的大量实验结果证明了所提方法的有效性。代码与数据集已开源。
原文摘要 · Abstract (English)
Instance segmentation of prohibited items in security X-ray images is a critical yet challenging task. This is mainly caused by the significant appearance gap between prohibited items in X-ray images and natural objects, as well as the severe overlapping among objects in X-ray images. To address these issues, we propose an occlusion-aware instance segmentation pipeline designed to identify prohibited items in X-ray images. Specifically, to bridge the representation gap, we integrate the Segment Anything Model (SAM) into our pipeline, taking advantage of its rich priors and zero-shot generalization capabilities. To address the overlap between prohibited items, we design an occlusion-aware bilayer mask decoder module that explicitly models the occlusion relationships. To supervise occlusion estimation, we manually annotated occlusion areas of prohibited items in two large-scale X-ray image segmentation datasets, PIDray and PIXray. We then reorganized these additional annotations together with the original information as two occlusion-annotated datasets, PIDray-A and PIXray-A. Extensive experimental results on these occlusion-annotated datasets demonstrate the effectiveness of our proposed method. The datasets and codes are available at: https://github.com/Ryh1218/Occ
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