A Cascaded Information Interaction Network for Precise Image Segmentation

TL;DR

This paper introduces a cascaded CNN with a Global Information Guidance Module that fuses multi-scale features to improve image segmentation accuracy in complex environments, outperforming existing methods.

Contribution

The paper presents a novel cascaded CNN architecture with a Global Information Guidance Module for enhanced feature fusion in image segmentation.

Findings

Achieves superior segmentation accuracy on benchmark datasets.

Effectively fuses low-level and high-level features across layers.

Outperforms state-of-the-art segmentation methods.

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.