Research on Weather Recognition Based on a Field Programmable Gate Array and Lightweight Convolutional Neural Network Article Swipe
Chen Liying
,
Fan Luo
,
Fei Wang
,
Liangfu Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14091740
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/electronics14091740
With the rapid development of deep learning, weather recognition has become a research hotspot in the field of computer vision, and the research on field programmable gate array (FPGA) acceleration based on deep learning algorithms has received more and more attention, based on which, we propose a method to implement deep neural networks for weather recognition in a small-scale FPGA. First, we train a deep separable convolutional neural network model for weather recognition to reduce the parameters and speed up the performance of hardware implementation. However, large-scale computation also brings the problem of excessive power consumption, which greatly limits the deployment of high-performance network models on mobile platforms. Therefore, we use a lightweight convolutional neural network approach to reduce the scale of computation, and the main idea of lightweight is to use fewer bits to store the weights. In addition, a hardware implementation of this model is proposed to speed up the operation and save on-chip resource consumption. Finally, the network model is deployed on a Xilinx ZYNQ xc7z020 FPGA to verify the accuracy of the recognition results, and the accelerated solution succeeds in achieving excellent performance with a speed of 108 FPS and 3.256 W of power consumption. The purpose of this design is to be able to accurately recognize the weather and deliver current environmental weather information to UAV (unmanned aerial vehicle) pilots and other staff who need to consider the weather, so that they can accurately grasp the current environmental weather conditions at any time. When the weather conditions change, the information can be obtained in a timely and effective manner to make the correct judgment, to ensure the flight of the UAV, and to avoid the equipment being affected by the weather leading to equipment damage and failure of the flight mission. With the help of this design, the UAV flight mission can be better completed.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics14091740
- https://www.mdpi.com/2079-9292/14/9/1740/pdf?version=1745495579
- OA Status
- gold
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409735612
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409735612Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/electronics14091740Digital Object Identifier
- Title
-
Research on Weather Recognition Based on a Field Programmable Gate Array and Lightweight Convolutional Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-24Full publication date if available
- Authors
-
Chen Liying, Fan Luo, Fei Wang, Liangfu LuList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics14091740Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/14/9/1740/pdf?version=1745495579Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2079-9292/14/9/1740/pdf?version=1745495579Direct OA link when available
- Concepts
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Convolutional neural network, Computer science, Field (mathematics), Gate array, Field-programmable gate array, Artificial neural network, Pattern recognition (psychology), Artificial intelligence, Computer architecture, Embedded system, Pure mathematics, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- References (count)
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26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_date | 2025-04-24 |
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| referenced_works | https://openalex.org/W2778845505, https://openalex.org/W6752735937, https://openalex.org/W3101451077, https://openalex.org/W2056149869, https://openalex.org/W2893202042, https://openalex.org/W2316103616, https://openalex.org/W2577696039, https://openalex.org/W2779517837, https://openalex.org/W2616549177, https://openalex.org/W6727208969, https://openalex.org/W6684563725, https://openalex.org/W2963125010, https://openalex.org/W2531409750, https://openalex.org/W2094756095, https://openalex.org/W2345185127, https://openalex.org/W2106158225, https://openalex.org/W2053058877, https://openalex.org/W2154673940, https://openalex.org/W2294037204, https://openalex.org/W2606267751, https://openalex.org/W4313260624, https://openalex.org/W4214604703, https://openalex.org/W2907579173, https://openalex.org/W2950248853, https://openalex.org/W2810857113, https://openalex.org/W2524428287 |
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