FSANet: Frequency-Spatial Aware Network for Image Segmentation

2026-09-15

AI Summary

针对图像分割中的遮挡、光照不良等问题，提出FSANet网络，通过结构先验模块、双域感知模块和边缘估计模块提升分割精度，并引入SceneX数据集以增强模型鲁棒性。

Abstract

Image segmentation remains challenging due to occlusions, poor lighting, and irregular structures. Although transformer-based methods achieve high accuracy, they rely heavily on long-range spatial features, leading to high computational costs and neglecting prior knowledge or noise patterns, resulting in missing details and unclear boundaries. To address these issues, we propose Frequency Spatial Aware Network (FSANet), which integrates prior knowledge with a dual-domain solver to sequentially adapt to diverse segmentation tasks. Specifically, we design three key modules: (1) Structure Prior Module, which recovers overlooked details; (2) Dual-Domain Awareness Module, which captures salient features while disentangling noise; and (3) Edge Estimation Module, which enhances edge awareness for more precise segmentation. In addition, the limited availability of comprehensive segmentation datasets covering various real-world scenarios hinders the performance of existing methods. To address this, we introduce SceneX, a novel open-source dataset featuring 10 challenging non-ideal scenarios, establishing a new benchmark for evaluating and improving the robustness and real-world applicability of the segmentation models. Extensive experiments demonstrate the efficiency and effectiveness of FSANet.

Problem

• Image Segmentation
• Occlusions
• Poor Lighting
• Irregular Structures
• Transformer-based Methods

Innovation

• Frequency-Spatial Aware Network
• Dual-Domain Solver
• SceneX Dataset