Optimized cross-module attention network and medium-scale dataset for effective fire detection
Khan, Zulfiqar Ahmad Ullah, Fath U Min Yar, Hikmat Ullah, Waseem Khan, Noman Kim, Min Je Baik, Sung Wook
Khan, Zulfiqar Ahmad
Ullah, Fath U Min
Yar, Hikmat
Ullah, Waseem
Khan, Noman
Kim, Min Je
Baik, Sung Wook
Supervisor
Department
Machine Learning
Embargo End Date
Type
Journal article
Date
2025
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Language
English
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Abstract
Over a decade, computer vision has shown a keen interest toward vision-based fire detection due to its wide range of applications. Primarily, fire detection relies on color features that inspired recent deep models to achieve reasonable performance. However, a perfect balance between high fire detection rate and computational complexity over mainstream surveillance setups is a challenging task. To establish a better tradeoff between model complexity and fire detection rate, this article develops an efficient and effective Cross Module Attention Network (CANet) for fire detection. CANet is developed from scratch with a squeezing and expansive paths to focus on the fire regions and its location. Next, the channel attention and Multi-Scale Feature Selection (MSFS) modules are integrated to accomplish the most important channels, selectively emphasize the contributions of feature maps, and enhance the discrimination potential of fire and non-fire objects. Furthermore, the CANet is optimized by removing a significant number of parameters for real-world applications. Finally, we introduce a challenging database for fire classification comprised of multiple classes and highly similar fire and non-fire object images. CANet improved accuracy by 2.5 % for the BWF, 2.2 % for the DQFF, 1.42 % for the LSFD, 1.8 % for the DSFD, and 1.14 % for the FG, Additionally, CANet achieved a 3.6 times higher FPS on resource-constrained devices compared to baseline methods.
Citation
Z. A. Khan et al., “Optimized cross-module attention network and medium-scale dataset for effective fire detection,” Pattern Recognit, vol. 161, p. 111273, May 2025, doi: 10.1016/J.PATCOG.2024.111273.
Source
Pattern Recognition
Conference
Keywords
Channel attention, Fire detection, Image classification, detection, Multi-scale feature selection
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Source
Publisher
Elsevier