ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and Recognition Article Swipe
Jianmei Zhang
,
Hongqing Zhu
,
Pengyu Wang
,
Xiaofeng Ling
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3050628
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3050628
Forest fire is becoming one of the most significant natural disasters at the expense of ecology and economy. In this article, we develop an effective SqueezeNet based asymmetric encoder-decoder U-shape architecture, Attention U-Net and SqueezeNet (ATT Squeeze U-Net), mainly functions as an extractor and a discriminator of forest fire. This model takes attention mechanism to highlight useful features and suppress irrelevant contents by embedding Attention Gate (AG) units in the skip connection of U-shape structure. In this way, salient features are emphasized so that the proposed method could be competent at forest fire segmentation tasks with a small number of parameters. Specifically, we first replace classical convolution layer by a depthwise one and engage a Channel Shuffle operation as a feature communicator in the Fire module of classical SqueezeNet. Then, this modified SqueezeNet is employed as a substitution of the encoder of Attention U-Net and a corresponding DeFire module designed is combined into the decoder as well. Finally, to classify true fire, we take use of a fragment of the encoder in ATT Squeeze U-Net. The experimental results of modified SqueezeNet integrated Attention U-Net show that a competitive accuracy at 0.93 and an average prediction time at 0.89 second per image are achieved for reliable real-time forest fire detection.
Related Topics
Concepts
Computer science
Convolution (computer science)
Feature (linguistics)
Embedding
Fire detection
Raster graphics
Discriminator
Artificial intelligence
Block (permutation group theory)
Encoder
Pattern recognition (psychology)
Simulation
Artificial neural network
Mathematics
Engineering
Telecommunications
Geometry
Operating system
Linguistics
Detector
Architectural engineering
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3050628
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09319207.pdf
- OA Status
- gold
- Cited By
- 136
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3120107013
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3120107013Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2021.3050628Digital Object Identifier
- Title
-
ATT Squeeze U-Net: A Lightweight Network for Forest Fire Detection and RecognitionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Jianmei Zhang, Hongqing Zhu, Pengyu Wang, Xiaofeng LingList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2021.3050628Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09319207.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/9312710/09319207.pdfDirect OA link when available
- Concepts
-
Computer science, Convolution (computer science), Feature (linguistics), Embedding, Fire detection, Raster graphics, Discriminator, Artificial intelligence, Block (permutation group theory), Encoder, Pattern recognition (psychology), Simulation, Artificial neural network, Mathematics, Engineering, Telecommunications, Geometry, Operating system, Linguistics, Detector, Architectural engineering, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
136Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 29, 2024: 36, 2023: 36, 2022: 26, 2021: 8Per-year citation counts (last 5 years)
- References (count)
-
50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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