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.

翻译：视觉感知在实现自主行为中起着关键作用，为复杂的多传感器系统提供了一种经济高效的替代方案。然而，在复杂场景中实现鲁棒的分割仍然是一个挑战。为解决这一问题，本文提出了一种与新型全局信息引导模块集成的级联卷积神经网络。该模块旨在跨多个层级有效融合低层纹理细节与高层语义特征，从而克服单尺度特征提取的固有局限性。这一架构创新显著提升了分割精度，尤其是在传统方法常常失效的视觉杂乱或模糊环境中。在基准图像分割数据集上的实验评估表明，所提出的框架实现了卓越的精度，超越了现有的最先进方法。结果凸显了该方法的有效性及其在实际机器人应用中部署的广阔潜力。