TY  - JOUR
AU  - Haider, Asad Ullah
AU  - Khan, Shadab
AU  - Ahmed, Muhammad Jamal
AU  - Khan, Taimur Ali
PY  - 2025
DA  - 2025/12/20
TI  - Strip Pooling Coordinate Attention with Directional Learning for Intelligent Fire Recognition in Smart Cities
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  - 2
IS  - 4
SP  - 263
EP  - 275
DO  - 10.62762/TSCC.2025.675097
UR  - https://www.icck.org/article/abs/TSCC.2025.675097
KW  - fire detection
KW  - directional attention
KW  - strip pooling
KW  - smart cities
KW  - anisotropic feature learning
KW  - multi-scale fusion
AB  - Fire detection in smart cities requires intelligent visual recognition systems capable of distinguishing fire from visually similar phenomena while maintaining real-time performance under diverse environmental conditions. Existing deep learning approaches employ attention mechanisms that aggregate spatial information isotropically, failing to capture the inherently directional characteristics of fire and smoke patterns. This paper presents DirFireNet, a novel fire detection framework that exploits directional fire dynamics through Strip Pooling Coordinate Attention (SPCA). Unlike conventional attention mechanisms, DirFireNet explicitly models vertical flame propagation and horizontal smoke dispersion via directional strip pooling operations that decompose features along horizontal and vertical axes. The framework integrates a progressive top-down fusion pathway with attention-guided weighting that synthesizes multi-scale representations from coarse to fine resolutions. Furthermore, dual global pooling captures complementary scene statistics, including holistic fire intensity and salient flame regions. Built upon the lightweight EfficientNetV2-S backbone, DirFireNet achieves superior accuracy while maintaining computational efficiency. Extensive experiments on the FD and BoWFire benchmark demonstrate state-of-the-art (SOTA) performance. Comprehensive ablation studies validate that directional attention contributes to accuracy gain, validating that attention mechanism provides strong inductive biases for intelligent fire recognition in smart city applications.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Haider2025Strip,
  author = {Asad Ullah Haider and Shadab Khan and Muhammad Jamal Ahmed and Taimur Ali Khan},
  title = {Strip Pooling Coordinate Attention with Directional Learning for Intelligent Fire Recognition in Smart Cities},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {4},
  pages = {263-275},
  doi = {10.62762/TSCC.2025.675097},
  url = {https://www.icck.org/article/abs/TSCC.2025.675097},
  abstract = {Fire detection in smart cities requires intelligent visual recognition systems capable of distinguishing fire from visually similar phenomena while maintaining real-time performance under diverse environmental conditions. Existing deep learning approaches employ attention mechanisms that aggregate spatial information isotropically, failing to capture the inherently directional characteristics of fire and smoke patterns. This paper presents DirFireNet, a novel fire detection framework that exploits directional fire dynamics through Strip Pooling Coordinate Attention (SPCA). Unlike conventional attention mechanisms, DirFireNet explicitly models vertical flame propagation and horizontal smoke dispersion via directional strip pooling operations that decompose features along horizontal and vertical axes. The framework integrates a progressive top-down fusion pathway with attention-guided weighting that synthesizes multi-scale representations from coarse to fine resolutions. Furthermore, dual global pooling captures complementary scene statistics, including holistic fire intensity and salient flame regions. Built upon the lightweight EfficientNetV2-S backbone, DirFireNet achieves superior accuracy while maintaining computational efficiency. Extensive experiments on the FD and BoWFire benchmark demonstrate state-of-the-art (SOTA) performance. Comprehensive ablation studies validate that directional attention contributes to accuracy gain, validating that attention mechanism provides strong inductive biases for intelligent fire recognition in smart city applications.},
  keywords = {fire detection, directional attention, strip pooling, smart cities, anisotropic feature learning, multi-scale fusion},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}