偏振驱动的伪装目标检测:具有迭代偏振特征增强的多模态融合网络。

Applied optics Pub Date : 2025-09-20 DOI:10.1364/AO.570214
Xiangyue Zhang, Jingyu Ru, Yihang Wang, Chengdong Wu
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引用次数: 0

摘要

复杂背景和动态光照条件下的伪装目标检测性能下降问题已成为光学成像与检测领域的一个难题。针对传统可见光成像方法由于无法区分材料和表面光学性质而容易失败的局限性,本文提出了一种偏振驱动的多模态融合网络(PMFNet)。通过极化特征的迭代增强,实现了高精度COD。首先,基于物体表面散射特性引起的偏振差,设计了特征整流模块;其次,建立了一种极化引导迭代细化机制,利用高分辨率极化特征动态校正RGB模式下的纹理退化。最后,引入极化自适应融合模块,通过细化极化信息,实现RGB特征的上下文感知互补增强,从而深度融合两种模式的互补特征。所提出的PMFNet在恶劣光照和复杂背景条件下具有鲁棒的检测性能。在公共数据集上的实验结果表明,所提出的PMFNet优于最先进的COD方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Polarization-driven camouflaged object detection: a multimodal fusion network with iterative polarimetric feature enhancement.

The performance degradation of camouflaged object detection (COD) under complex backgrounds and dynamic illumination conditions has become a challenging issue in optical imaging and detection. To address the limitation of traditional visible-light imaging methods, which easily fail due to their inability to differentiate material and surface optical properties, a polarization-driven multimodal fusion network (PMFNet) is proposed in this paper. High-precision COD is achieved through iterative enhancement of polarization features. First, a feature rectification module is designed based on polarization differences induced by the surface scattering properties of objects. Second, a polarization-guided iterative refinement mechanism is developed, dynamically correcting texture degradation in RGB modality by employing high-resolution polarization features. Finally, a polarization adaptive fusion module is introduced to achieve context-aware complementary enhancement of RGB features through refined polarization information, thus deeply fusing complementary features of the two modalities. The proposed PMFNet demonstrates robust detection performance under adverse illumination and complex background conditions. Experimental results on public datasets demonstrate that the proposed PMFNet outperforms state-of-the-art COD methods.

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