{
  "abstract": "Invoice seal detection plays an important role in intelligent invoice processing by improving review efficiency and reducing potential financial risks. However, existing methods still face challenges in multi-scale feature representation, complex background interference, and balancing detection accuracy with computational efficiency. In this study, a lightweight detection model, ISD-YOLO, is proposed for invoice seal detection. Specifically, the C3k2-ConvFormer-CGLU module (C3k2_CFC) is designed to enhance multi-scale feature representation by combining local feature enhancement and gated feature selection. A C2BRA module based on bi-level routing attention is introduced to improve the extraction of discriminative seal features by dynamically selecting important regions. In addition, a deformable attention module (DAttention) is incorporated to enhance the focus on key regions and reduce background interference. Furthermore, a lightweight detection head (EfficientHead) is developed to reduce computational complexity while maintaining detection performance, providing potential for resource-constrained applications. Experiments were conducted on a dataset consisting of public seal images and real invoice images. Compared with the baseline model, ISD-YOLO improves mAP50 and mAP50:95 by 2.1% and 2.3%, respectively. Compared with existing detection methods, ISD-YOLO achieves an F1-score of 95.3% and an mAP50 of 97.2%, while requiring only 2.3 M parameters and 5.0 GFLOPs. The results demonstrate that ISD-YOLO achieves a favorable balance between detection accuracy and computational efficiency under the evaluated invoice seal detection conditions. Its lightweight design indicates potential for resource-constrained applications, while further validation under broader scenarios and on actual edge devices is still required.",
  "abstract_shown": true,
  "affiliations": [
    "Hengshui College of Vocational Technology, Hengshui, China",
    "North China Institute of Aerospace Engineering, Langfang, China"
  ],
  "article_type": "Research Article",
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  "author_total": 5,
  "authors": [
    {
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        0
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      "name": "Xiaosi Song"
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    {
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        1
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      "name": "Xiaohong Zhao"
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    {
      "name": "Caihua Ma"
    },
    {
      "name": "Wenpei Xu"
    },
    {
      "name": "Peng Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/44ymz7e1mgq2s.bib",
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  "doi": "10.1371/journal.pone.0358190",
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  "id": "44ymz7e1mgq2s",
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  "licence": "CC-BY-4.0",
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  "native_subjects": [
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358190&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-11",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/44ymz7e1mgq2s.ris",
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    {
      "first_seen": "2026-10-30T12:17:56Z",
      "native_id": "10.1371/journal.pone.0358190",
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      "url": "https://doi.org/10.1371/journal.pone.0358190"
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  "subjects": [
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      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  "title": "ISD-YOLO: A lightweight and efficient feature extraction network for invoice seal detection",
  "title_text": "ISD-YOLO: A lightweight and efficient feature extraction network for invoice seal detection",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:56Z",
  "url": "https://research.draconitic.com/paper/44ymz7e1mgq2s",
  "venue": "PLOS One",
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