A Human Target Infrared Image Segmentation Approach Based on Convolution Neural Network Article Swipe
Chao Liu
,
Qingping Hu
,
Yuan Yao
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1507/9/092004
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/1507/9/092004
In order to effectively segment the human target under complex background constraints, we present an infrared target segmentation method based on deep convolution neural network, and proposes the loss function based on the intersection-over-union for network optimization. Firstly, we design the network architecture which consists of a contracting path to capture the feature content and a symmetric expanding path that enables precise localization. And then rely on the powerful data amplification technology to effectively train the available sample data. The experimental results show that the network can make full use of the prior information of the data to study the characteristics of the human target, which can use less training data for end-to-end training in the human body target infrared image segmentation. And segmentation effect is superior to the traditional image segmentation algorithm. In addition, the network segmentation speed is very fast, 320 ×256 size image segmentation takes less than 0.2 seconds, to meet the human body target image segmentation of the effectiveness and real-time needs.
Related Topics
Concepts
Artificial intelligence
Computer science
Segmentation
Pattern recognition (psychology)
Convolutional neural network
Computer vision
Image segmentation
Scale-space segmentation
Intersection (aeronautics)
Convolution (computer science)
Segmentation-based object categorization
Artificial neural network
Feature (linguistics)
Minimum spanning tree-based segmentation
Path (computing)
Geography
Philosophy
Linguistics
Cartography
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/1507/9/092004
- OA Status
- diamond
- References
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3041266590
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3041266590Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/1507/9/092004Digital Object Identifier
- Title
-
A Human Target Infrared Image Segmentation Approach Based on Convolution Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-01Full publication date if available
- Authors
-
Chao Liu, Qingping Hu, Yuan YaoList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/1507/9/092004Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1742-6596/1507/9/092004Direct OA link when available
- Concepts
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Artificial intelligence, Computer science, Segmentation, Pattern recognition (psychology), Convolutional neural network, Computer vision, Image segmentation, Scale-space segmentation, Intersection (aeronautics), Convolution (computer science), Segmentation-based object categorization, Artificial neural network, Feature (linguistics), Minimum spanning tree-based segmentation, Path (computing), Geography, Philosophy, Linguistics, Cartography, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
4Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.speed | 139 |
| abstract_inverted_index.study | 99 |
| abstract_inverted_index.takes | 148 |
| abstract_inverted_index.train | 75 |
| abstract_inverted_index.under | 9 |
| abstract_inverted_index.which | 44, 106 |
| abstract_inverted_index.×256 | 144 |
| abstract_inverted_index.design | 40 |
| abstract_inverted_index.effect | 125 |
| abstract_inverted_index.method | 19 |
| abstract_inverted_index.needs. | 166 |
| abstract_inverted_index.neural | 24 |
| abstract_inverted_index.sample | 78 |
| abstract_inverted_index.target | 8, 17, 119, 158 |
| abstract_inverted_index.capture | 51 |
| abstract_inverted_index.complex | 10 |
| abstract_inverted_index.content | 54 |
| abstract_inverted_index.enables | 61 |
| abstract_inverted_index.feature | 53 |
| abstract_inverted_index.network | 36, 42, 86, 137 |
| abstract_inverted_index.precise | 62 |
| abstract_inverted_index.present | 14 |
| abstract_inverted_index.results | 82 |
| abstract_inverted_index.segment | 5 |
| abstract_inverted_index.target, | 105 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Firstly, | 38 |
| abstract_inverted_index.consists | 45 |
| abstract_inverted_index.function | 30 |
| abstract_inverted_index.infrared | 16, 120 |
| abstract_inverted_index.network, | 25 |
| abstract_inverted_index.powerful | 69 |
| abstract_inverted_index.proposes | 27 |
| abstract_inverted_index.seconds, | 152 |
| abstract_inverted_index.superior | 127 |
| abstract_inverted_index.training | 110, 114 |
| abstract_inverted_index.addition, | 135 |
| abstract_inverted_index.available | 77 |
| abstract_inverted_index.expanding | 58 |
| abstract_inverted_index.real-time | 165 |
| abstract_inverted_index.symmetric | 57 |
| abstract_inverted_index.algorithm. | 133 |
| abstract_inverted_index.background | 11 |
| abstract_inverted_index.end-to-end | 113 |
| abstract_inverted_index.technology | 72 |
| abstract_inverted_index.contracting | 48 |
| abstract_inverted_index.convolution | 23 |
| abstract_inverted_index.effectively | 4, 74 |
| abstract_inverted_index.information | 94 |
| abstract_inverted_index.traditional | 130 |
| abstract_inverted_index.architecture | 43 |
| abstract_inverted_index.constraints, | 12 |
| abstract_inverted_index.experimental | 81 |
| abstract_inverted_index.segmentation | 18, 124, 132, 138, 147, 160 |
| abstract_inverted_index.amplification | 71 |
| abstract_inverted_index.effectiveness | 163 |
| abstract_inverted_index.localization. | 63 |
| abstract_inverted_index.optimization. | 37 |
| abstract_inverted_index.segmentation. | 122 |
| abstract_inverted_index.characteristics | 101 |
| abstract_inverted_index.intersection-over-union | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.06242601 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |