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Related Experiment Video

Updated: Jul 7, 2026

Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

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Smoke-Aware Global-Interactive Non-Local Network for Smoke Semantic Segmentation.

Lin Zhang, Jing Wu, Feiniu Yuan

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 5, 2024

Summary

This summary is machine-generated.

A new Smoke-Aware Global-Interactive Non-local Network (SAGINN) improves smoke semantic segmentation (SSS) for intelligent fire detection. This network accurately locates smoke and refines boundaries, even with challenging smoke-like objects.

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Area of Science:

Computer Vision
Artificial Intelligence
Fire Safety Engineering

Background:

Smoke semantic segmentation (SSS) is challenging due to smoke's non-rigid, translucent, and variable nature.
Accurate smoke detection is crucial for intelligent fire detection systems.

Purpose of the Study:

To propose a novel network, Smoke-Aware Global-Interactive Non-local Network (SAGINN), for accurate smoke semantic segmentation.
To enhance the robustness and accuracy of smoke detection in complex real-world scenes.

Main Methods:

Developed a SAGINN combining convolutional and transformer approaches for simultaneous local and global information capture.
Introduced a Global-Interactive Non-local (GINL) module for multi-scale feature interaction and robustness.
Designed a Pyramid High-level Semantic Aggregation (PHSA) module to mitigate interference from smoke-like objects.
Proposed a novel Smoke-aware loss (SAL) function for differential object weighting.

Main Results:

SAGINN achieved an 83% average mIoU on the SYN70K dataset.
Demonstrated accuracy improvements of approximately 0.5% on SMOKE5K.
Achieved finer smoke boundaries and more accurate localization, outperforming existing methods.

Conclusions:

The proposed SAGINN effectively addresses the challenges of smoke semantic segmentation.
SAGINN shows strong generalization ability on both synthetic and real-world data.
The network contributes to advancing intelligent fire detection capabilities.