Automated garden-insect recognition using improved lightweight convolution network Article Swipe
Zhankui Yang
,
Xinting Yang
,
Ming Li
,
Wenyong Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.inpa.2021.12.006
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.inpa.2021.12.006
Automated recognition of insect category, which currently is performed mainly by agriculture experts, is a challenging problem that has received increasing attention in recent years. The goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile terminals. State-of-the-art lightweight convolutional neural networks (such as SqueezeNet and ShuffleNet) have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters, thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited memory. In this research, we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational cost. In addition, we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the time-delay problems in the field. Experiments demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64% with less training time relative to other classical convolutional neural networks. We have also verified the results that the improved SqueezeNet model has a 2.3% higher than of the original model in the open insect data IP102.
Related Topics
Concepts
Computer science
Convolutional neural network
Deep learning
Artificial neural network
Artificial intelligence
Feature (linguistics)
Convolution (computer science)
Field (mathematics)
Mobile device
Feature extraction
Recurrent neural network
Real-time computing
Pattern recognition (psychology)
Machine learning
Operating system
Mathematics
Linguistics
Pure mathematics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.inpa.2021.12.006
- OA Status
- gold
- Cited By
- 13
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206730314
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4206730314Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.inpa.2021.12.006Digital Object Identifier
- Title
-
Automated garden-insect recognition using improved lightweight convolution networkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-31Full publication date if available
- Authors
-
Zhankui Yang, Xinting Yang, Ming Li, Wenyong LiList of authors in order
- Landing page
-
https://doi.org/10.1016/j.inpa.2021.12.006Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.inpa.2021.12.006Direct OA link when available
- Concepts
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Computer science, Convolutional neural network, Deep learning, Artificial neural network, Artificial intelligence, Feature (linguistics), Convolution (computer science), Field (mathematics), Mobile device, Feature extraction, Recurrent neural network, Real-time computing, Pattern recognition (psychology), Machine learning, Operating system, Mathematics, Linguistics, Pure mathematics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2023: 7, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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