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A Cascaded Information Interaction Network for Precise Image Segmentation

Hewen Xiao, Jie Mei, Guangfu Ma, Weiren Wu

TL;DR

This work tackles robust image segmentation in complex environments by presenting a Cascaded Information Interaction Network guided by a Global Information Guidance Module. It fuses low-level texture and high-level semantic information through multi-scale cascaded interactions, powered by a Swin Transformer encoder, to preserve detail while maintaining global context. Empirical results on five standard datasets show state-of-the-art performance and strong robustness, with notable gains on cluttered and blurred scenes and potential for real-time robotic applications. Overall, the approach delivers precise segmentation with improved cross-scale information exchange, advancing autonomous perception capabilities.

Abstract

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.

A Cascaded Information Interaction Network for Precise Image Segmentation

TL;DR

This work tackles robust image segmentation in complex environments by presenting a Cascaded Information Interaction Network guided by a Global Information Guidance Module. It fuses low-level texture and high-level semantic information through multi-scale cascaded interactions, powered by a Swin Transformer encoder, to preserve detail while maintaining global context. Empirical results on five standard datasets show state-of-the-art performance and strong robustness, with notable gains on cluttered and blurred scenes and potential for real-time robotic applications. Overall, the approach delivers precise segmentation with improved cross-scale information exchange, advancing autonomous perception capabilities.

Abstract

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.
Paper Structure (7 sections, 8 equations, 2 figures)

This paper contains 7 sections, 8 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Method
  4. Experimental Results
  5. Experimental Setup
  6. Comparisons to the State-of-the-Arts
  7. Conclusion

Figures (2)

  • Figure 1: An overview of the proposed network framework
  • Figure 2: Visual comparison of saliency maps with state-of-the-art methods. From left to right: Input image, Ground truth, Ours, DNA, CII, MSFNet, VST and ITSD. Our approach consistently produces the best results.