ISD-YOLO: A lightweight and efficient feature extraction network for invoice seal detection

Xiaosi Song, Xiaohong Zhao, Caihua Ma, Wenpei Xu and Peng Shen

PLOS ONE, 2026, vol. 21, issue 9, 1-31

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.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0358190

DOI: 10.1371/journal.pone.0358190

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