通过级联网络融合细节与语义信息，提升复杂场景下图像分割精度。
A Cascaded Information Interaction Network for Precise Image Segmentation
- 设计多层信息交互模块，融合低层纹理与高层语义特征。
- 在多个基准数据集上超越现有最优方法，显著提升分割准确率。
- 适合需要高精度分割的机器人视觉应用，尤其在模糊或杂乱环境。
视觉感知在实现自主行为中起关键作用，为复杂多传感器系统提供了一种低成本、高效的替代方案。然而，在复杂场景中实现鲁棒的分割仍具挑战。为此，本文提出一种集成新型全局信息引导模块的级联卷积神经网络。该模块能有效融合多层中的低层纹理细节与高层语义特征，克服单尺度特征提取的固有局限。这一架构创新显著提升了分割精度，尤其在视觉杂乱或模糊环境中表现优异，传统方法常在此类场景中失效。在多个基准图像分割数据集上的实验评估表明，所提框架实现了更优的精度，超越现有最先进方法。结果验证了该方法的有效性及其在实际机器人应用中的广阔前景。
原文摘要 · Abstract (English)
Visual perception plays a pivotal role in enabling autonomous behavior, offering a cost-effective and efficient alternative to complex multi-sensor systems. However, robust segmentation remains a challenge in complex scenarios. To address this, this paper proposes a cascaded convolutional neural network integrated with a novel Global Information Guidance Module. This module is designed to effectively fuse low-level texture details with high-level semantic features across multiple layers, thereby overcoming the inherent limitations of single-scale feature extraction. This architectural innovation significantly enhances segmentation accuracy, particularly in visually cluttered or blurred environments where traditional methods often fail. Experimental evaluations on benchmark image segmentation datasets demonstrate that the proposed framework achieves superior precision, outperforming existing state-of-the-art methods. The results highlight the effectiveness of the approach and its promising potential for deployment in practical robotic applications.
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