Fire detection models based on attention mechanisms and multiscale features Article Swipe
Shunxiang Zhang
,
Meng Chen
,
Kuan‐Ching Li
,
Hua Wen
,
Liang Sun
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2298/csis241225059z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.2298/csis241225059z
Fire detection is critical in applications such as fire management and building safety, but dispersion and blurring of flame and smoke boundaries can present challenges. Multiple upsampling and downsampling operations can blur the localisation signals, thus reducing accuracy and efficiency. To address this problem, we propose the AMMF(Attention Mechanisms and Multiscale Features) detection model, which integrates an attention mechanism and multi-scale feature fusion to improve accuracy and real-time performance. The model incorporates a dynamic sparse attention mechanism in the backbone network to enhance feature capture and restructures the neck network using CepBlock and MPFusion modules for better feature fusion. MDPIoU loss and Slideloss are then utilised to reduce the bounding box regression error and address the sample imbalance problem respectively. In addition, parameters are shared by merging 3?3 convolutional branches, which optimises the detection head and improves computational efficiency. The experimental results show that AMMF-Detection can significantly improve the detection speed and accuracy on the public dataset.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2298/csis241225059z
- http://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500059Z
- OA Status
- diamond
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413704130
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413704130Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2298/csis241225059zDigital Object Identifier
- Title
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Fire detection models based on attention mechanisms and multiscale featuresWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Shunxiang Zhang, Meng Chen, Kuan‐Ching Li, Hua Wen, Liang SunList of authors in order
- Landing page
-
https://doi.org/10.2298/csis241225059zPublisher landing page
- PDF URL
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https://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500059ZDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.doiserbia.nb.rs/ft.aspx?id=1820-02142500059ZDirect OA link when available
- Concepts
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Computer science, Fire detection, Artificial intelligence, Physics, ThermodynamicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| citation_normalized_percentile.value | 0.46960024 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |