Prohibited Items Segmentation via Occlusion-aware Bilayer Modeling

• URL: http://arxiv.org/abs/2506.11661v1
• Date: Fri, 13 Jun 2025 10:48:55 GMT
• Title: Prohibited Items Segmentation via Occlusion-aware Bilayer Modeling
• Authors: Yunhan Ren, Ruihuang Li, Lingbo Liu, Changwen Chen,
• Abstract summary: Instance segmentation of prohibited items in security X-ray images is a critical yet challenging task.<n>This is mainly caused by the significant appearance gap between prohibited items in X-ray images and natural objects.<n>We propose an occlusion-aware instance segmentation pipeline designed to identify prohibited items in X-ray images.
• Score: 31.75566926799125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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