DFRNet: A Semantic Segmentation Method Inspired with Physical Mechanism of Diffusion-Focus

HUANG Yi-sha; JIANG Lin; GUAN Ya-fei; ZHANG Ya-sha; LIANG Xin; ZENG Wei-hao; FANG Xiao-ping

doi:10.12263/DZXB.20250186

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DFRNet: A Semantic Segmentation Method Inspired with Physical Mechanism of Diffusion-Focus

Large-Scale Models and the Internet | 更新时间：2025-10-16

- DFRNet: A Semantic Segmentation Method Inspired with Physical Mechanism of Diffusion-Focus
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 1755-1770(2025)
- 作者机构：
  
  1.湖南工商大学人工智能与先进计算学院，湖南长沙 410000
  2.湘江实验室，湖南长沙 410000
  3.湖南工商大学智能工程与制造学院，湖南长沙 410000
  4.湖南工商大学数学与统计学院，湖南长沙 410000
- 作者简介：
- 基金信息：
  
  Major Project of Xiangjiang Laboratory(23XJ01003;23XJ01009);Key Scientific Research Project of Hunan Provincial Department of Education(22A0441)
- DOI：10.12263/DZXB.20250186
  CLC： TP391;
- Received：12 March 2025，
  
  Revised：2025-06-10，
  
  Published：25 June 2025
- 稿件说明：
移动端阅览
黄依莎, 姜林, 管亚菲, 等. DFRNet：融合扩散-聚焦物理机制的语义分割模型研究[J]. 电子学报, 2025, 53(06): 1755-1770.

HUANG Yi-sha, JIANG Lin, GUAN Ya-fei, et al. DFRNet: A Semantic Segmentation Method Inspired with Physical Mechanism of Diffusion-Focus[J]. Acta Electronica Sinica, 2025, 53(06): 1755-1770.
黄依莎, 姜林, 管亚菲, 等. DFRNet：融合扩散-聚焦物理机制的语义分割模型研究[J]. 电子学报, 2025, 53(06): 1755-1770. DOI：10.12263/DZXB.20250186

HUANG Yi-sha, JIANG Lin, GUAN Ya-fei, et al. DFRNet: A Semantic Segmentation Method Inspired with Physical Mechanism of Diffusion-Focus[J]. Acta Electronica Sinica, 2025, 53(06): 1755-1770. DOI：10.12263/DZXB.20250186

摘要

针对图像语义分割任务中下采样过程导致的信息丢失问题，以及现有上采样方法在处理复杂场景中普遍存在的全局信息丢失、细节模糊、生成过程不稳定及信息冗余等局限，本文提出了一种融合物理扩散-聚焦机制的轻量级语义分割模型——DFRNet.该模型引入了液体表面张力的扩散-聚焦机制，并进一步设计了动态上下文窗口选择（Dynamic context Window Selection，DWS）模块作为调节优化机制，从而实现了物理启发的能量传播上采样（Physics-Inspired Energy Propagation Upsampling，PIEPU）新方法.该方法包含扩散、聚焦与调节三大机制，分别承担全局上下文信息扩展、关键区域特征增强与信息流动优化的功能，协同提升模型在复杂场景下的细粒度感知与语义一致性表达能力.在7种类别14个数据集的验证表明：所提DFRNet在mIou、

分数和Accuracy指标上均领先于其他先进模型，且在不同数据集上，mIou提升幅度为0.165%~4.259%；

分数提升为0.140%~2.888%；Accuracy提升为0.035%~1.386%，验证了方法在多样化任务环境下的鲁棒性与泛化能力.本文模型参数量仅为3.34 MB，可满足轻量化实时应用.

Abstract

To address the information loss induced by downsampling in image semantic segmentation tasks

as well as the widespread limitations of existing upsampling methods: such as inadequate global perception

blurred fine-grained reconstruction

unstable generation processes

and redundant information handling in various scenarios

this paper proposes a lightweight semantic segmentation model

DFRNet

which incorporates a physics-inspired diffusion-focusing mechanism. Specifically

inspired by the surface tension of liquids

the model introduces a diffusion-focusing mechanism and designs a dynamic context window selection (DWS) module to optimize information flow

thereby implementing the physics-inspired energy propagation upsampling (PIEPU) framework. PIEPU comprises three core modules: diffusion

focusing

and regulation. These modules collaborati

vely enhance global contextual propagation

critical region feature reinforcement

and optimized information flow

thereby significantly improving fine-grained perception and semantic consistency across complex scenarios. Extensive experiments conducted on 14 datasets covering 7 semantic categories demonstrate that DFRNet consistently achieves superior performance over state-of-the-art methods in terms of mean intersection over union (mIoU)

score

and Accuracy. Specifically

mIoU improvements range from 0.165% to 4.259%

score gains span 0.140% to 2.888%

and Accuracy enhancements vary from 0.035% to 1.386% across diverse datasets. These results validate the robustness and generalization capability of the proposed approach. Notably

DFRNet has a model size of only 3.34 MB

making it suitable for lightweight real-time applications.

关键词

Keywords

references

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