融合全局和局部信息的实时烟雾分割算法

张欣雨; 梁煜; 张为

doi:10.19665/j.issn1001-2400.20230405

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融合全局和局部信息的实时烟雾分割算法

计算机科学与技术 | 更新时间：2024-04-03

- 融合全局和局部信息的实时烟雾分割算法
- Real-time smoke segmentation algorithm combining global and local information
- 西安电子科技大学学报 2024年51卷第1期页码：147-156
- 作者机构：
  
  天津大学微电子学院,天津 300072
- 作者简介：
  
  [ "张欣雨(1999—),女,天津大学硕士研究生,E-mail:[email protected]；" ]
  [ "梁煜(1975—),男,副教授,E-mail:[email protected]" ]
  张为(1975—),男,教授,E-mail:[email protected]
- 基金信息：
  
  天津市新一代人工智能科技重大专项(19ZXZNGX00030)
- DOI：10.19665/j.issn1001-2400.20230405
  中图分类号： TP391.41
- 纸质出版日期：2024-1-20，
  
  网络出版日期：2023-9-6，
  
  收稿日期：2023-1-13，
扫描看全文
张欣雨, 梁煜, 张为. 融合全局和局部信息的实时烟雾分割算法[J]. 西安电子科技大学学报, 2024,51(1):147-156.

Xinyu ZHANG, Yu LIANG, Wei ZHANG. Real-time smoke segmentation algorithm combining global and local information[J]. Journal of Xidian University, 2024,51(1):147-156.
张欣雨, 梁煜, 张为. 融合全局和局部信息的实时烟雾分割算法[J]. 西安电子科技大学学报, 2024,51(1):147-156. DOI： 10.19665/j.issn1001-2400.20230405.

Xinyu ZHANG, Yu LIANG, Wei ZHANG. Real-time smoke segmentation algorithm combining global and local information[J]. Journal of Xidian University, 2024,51(1):147-156. DOI： 10.19665/j.issn1001-2400.20230405.

摘要

针对烟雾形状不规则、呈半透明状且边界模糊导致烟雾分割困难的问题

提出一种融合全局和局部信息的双分支实时烟雾分割算法。该算法设计了轻量级的Transformer分支和卷积神经网络分支分别提取烟雾的全局特征和局部特征

Transformer分支和卷积神经网络分支共同作用

可以在充分学习烟雾的长距离像素依赖关系的同时保留烟雾细节信息

从而准确区分烟雾和背景像素

改善烟雾分割效果。同时该结构可以满足实际烟雾检测任务的实时性要求;基于多层感知机的解码器充分利用不同尺度的烟雾特征图

并进一步建模烟雾全局上下文信息

增强模型对多尺度烟雾的感知能力

从而提升烟雾分割精度;而且解码器结构简单

可以降低解码器部分的计算量。该算法在自建烟雾分割数据集上的平均交并比为92.88%

模型参数量为2.96 M

推理速度为56.94帧/s。该算法在公开数据集上的综合性能优于其他烟雾检测算法。实验结果表明

该算法分割烟雾的准确率高

推理速度快

可以满足实际烟雾检测任务的准确性和实时性需求。

Abstract

The smoke segmentation is challenging because the smoke is irregular and translucent and the boundary is fuzzy.A dual-branch real-time smoke segmentation algorithm based on global and local information is proposed to solve this problem.In this algorithm

a lightweight Transformer branch and a convolutional neural networks branch are designed to extract the global and local features of smoke respectively

which can fully learn the long-distance pixel dependence of smoke and retain the details of smoke.It can distinguish smoke and background accurately and improve the accuracy of smoke segmentation.It can satisfy the real-time requirement of the actual smoke detection tasks.The multilayer perceptron decoder makes full use of multi-scale smoke features and further models the global context information of smoke.It can enhance the perception of multi-scale smoke

and thus improve the accuracy of smoke segmentation.The simple structure can reduce the computation of the decoder.The algorithm reaches 92.88% mean intersection over union on the self-built smoke segmentation dataset with 2.96M parameters and a speed of 56.94 frames per second.The comprehensive performance of the proposed algorithm is better than that of other smoke detection algorithms on public dataset.Experimental results show that the algorithm has a high accuracy and fast inference speed.The algorithm can meet the accuracy and real-time requirements of actual smoke detection tasks.

关键词

烟雾分割Transformer卷积神经网络双分支

Keywords

smoke segmentationTransformerconvolutional neural networksdual-branch

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