Intelligent Fire Recognition for Surveillance Control Using Cascaded Multi-Scale Attention Framework

Ikram Majeed Khan; Faryal Zahoor

doi:10.62762/TSCC.2025.862776

Volume 3, Issue 1, ICCK Transactions on Sensing, Communication, and Control

Volume 3, Issue 1, 2026

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ICCK Transactions on Sensing, Communication, and Control, Volume 3, Issue 1, 2026: 15-26

Free to Read | Research Article | 13 February 2026

Intelligent Fire Recognition for Surveillance Control Using Cascaded Multi-Scale Attention Framework

Ikram Majeed Khan 1

Faryal Zahoor 2 *

1 Department of Computer Science, Coventry University, Coventry CV1 5FB, United Kingdom

2 BRAINS Institute Peshawar, Peshawar 25000, Pakistan

* Corresponding Author: Faryal Zahoor, [email protected]

DOI: 10.62762/TSCC.2025.862776

ARK: ark:/57805/tscc.2025.862776

Received: 17 December 2025, Accepted: 12 January 2026, Published: 13 February 2026

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Abstract

Fire incidents cause devastating environmental damage and human casualties, necessitating robust automated detection systems. Existing fire recognition methods struggle with visual ambiguities, illumination variations, and computational constraints, while current attention mechanisms lack hierarchical integration for comprehensive feature refinement. We propose a cascaded multi-attention architecture that combines Multi-Scale Strip Attention (MSSA), Optimized Spatial Attention (OSA), and the Convolutional Block Attention Module (CBAM) to enhance fire detection. MSSA employs three-scale orthogonal strip pooling to capture fire patterns across varying spatial extents through horizontal and vertical feature decomposition. OSA employs optimized convolutions rather than conventional kernels, reducing computational cost while maintaining spatial localization accuracy. CBAM applies sequential channel- and spatial-level attention for comprehensive feature recalibration. Operating on the EfficientNetB7 backbone, the cascaded design progressively refines representations through complementary mechanisms. Systematic ablation studies validate individual module contributions, while Grad-CAM visualizations confirm precise localization of fire regions. Extensive experiments on the FD and BoWFire datasets demonstrate superior performance compared to state-of-the-art approaches.

Graphical Abstract

Intelligent Fire Recognition for Surveillance Control Using Cascaded Multi-Scale Attention Framework

Keywords

fire detection

strip pooling

surveillance monitoring

intelligent sensing

safety control

visual recognition

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

AI Use Statement

The authors declare that no generative AI was used in the preparation of this manuscript.

Ethical Approval and Consent to Participate

Not applicable.

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Khan, I. M., & Zahoor, F. (2026). Intelligent Fire Recognition for Surveillance Control Using Cascaded Multi-Scale Attention Framework. ICCK Transactions on Sensing, Communication, and Control, 3(1), 15–26. https://doi.org/10.62762/TSCC.2025.862776

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TY  - JOUR
AU  - Khan, Ikram Majeed
AU  - Zahoor, Faryal
PY  - 2026
DA  - 2026/02/13
TI  - Intelligent Fire Recognition for Surveillance Control Using Cascaded Multi-Scale Attention Framework
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 3
IS  - 1
SP  - 15
EP  - 26
DO  - 10.62762/TSCC.2025.862776
UR  - https://www.icck.org/article/abs/TSCC.2025.862776
KW  - fire detection
KW  - strip pooling
KW  - surveillance monitoring
KW  - intelligent sensing
KW  - safety control
KW  - visual recognition
AB  - Fire incidents cause devastating environmental damage and human casualties, necessitating robust automated detection systems. Existing fire recognition methods struggle with visual ambiguities, illumination variations, and computational constraints, while current attention mechanisms lack hierarchical integration for comprehensive feature refinement. We propose a cascaded multi-attention architecture that combines Multi-Scale Strip Attention (MSSA), Optimized Spatial Attention (OSA), and the Convolutional Block Attention Module (CBAM) to enhance fire detection. MSSA employs three-scale orthogonal strip pooling to capture fire patterns across varying spatial extents through horizontal and vertical feature decomposition. OSA employs optimized convolutions rather than conventional kernels, reducing computational cost while maintaining spatial localization accuracy. CBAM applies sequential channel- and spatial-level attention for comprehensive feature recalibration. Operating on the EfficientNetB7 backbone, the cascaded design progressively refines representations through complementary mechanisms. Systematic ablation studies validate individual module contributions, while Grad-CAM visualizations confirm precise localization of fire regions. Extensive experiments on the FD and BoWFire datasets demonstrate superior performance compared to state-of-the-art approaches.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Khan2026Intelligen,
  author = {Ikram Majeed Khan and Faryal Zahoor},
  title = {Intelligent Fire Recognition for Surveillance Control Using Cascaded Multi-Scale Attention Framework},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {15-26},
  doi = {10.62762/TSCC.2025.862776},
  url = {https://www.icck.org/article/abs/TSCC.2025.862776},
  abstract = {Fire incidents cause devastating environmental damage and human casualties, necessitating robust automated detection systems. Existing fire recognition methods struggle with visual ambiguities, illumination variations, and computational constraints, while current attention mechanisms lack hierarchical integration for comprehensive feature refinement. We propose a cascaded multi-attention architecture that combines Multi-Scale Strip Attention (MSSA), Optimized Spatial Attention (OSA), and the Convolutional Block Attention Module (CBAM) to enhance fire detection. MSSA employs three-scale orthogonal strip pooling to capture fire patterns across varying spatial extents through horizontal and vertical feature decomposition. OSA employs optimized convolutions rather than conventional kernels, reducing computational cost while maintaining spatial localization accuracy. CBAM applies sequential channel- and spatial-level attention for comprehensive feature recalibration. Operating on the EfficientNetB7 backbone, the cascaded design progressively refines representations through complementary mechanisms. Systematic ablation studies validate individual module contributions, while Grad-CAM visualizations confirm precise localization of fire regions. Extensive experiments on the FD and BoWFire datasets demonstrate superior performance compared to state-of-the-art approaches.},
  keywords = {fire detection, strip pooling, surveillance monitoring, intelligent sensing, safety control, visual recognition},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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