SGI-YOLOv9: an effective method for crucial components detection in the power distribution network Article Swipe
Mingxu Yang
,
Bojian Chen
,
Chenxiang Lin
,
Wenxu Yao
,
Yangdi Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2024.1517177
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fphy.2024.1517177
The detection of crucial components in the power distribution network is of great significance for ensuring the safe operation of the power grid. However, the challenges posed by complex environmental backgrounds and the difficulty of detecting small objects remain key obstacles for current technologies. Therefore, this paper proposes a detection method for crucial components in the power distribution network based on an improved YOLOv9 model, referred to as SGI-YOLOv9. This method effectively reduces the loss of fine-grained features and improves the accuracy of small objects detection by introducing the SPDConv++ downsampling module. Additionally, a global context fusion module is designed to model global information using a self-attention mechanism in both spatial and channel dimensions, significantly enhancing the detection robustness in complex backgrounds. Furthermore, this paper proposes the Inner-PIoU loss function, which combines the advantages of Powerful-IoU and Inner-IoU to improve the convergence speed and regression accuracy of bounding boxes. To verify the effectiveness of SGI-YOLOv9, extensive experiments are conducted on the CPDN dataset and the PASCAL VOC 2007 dataset. The experimental results demonstrate that SGI-YOLOv9 achieves a significant improvement in accuracy for small object detection tasks, with an mAP@50 of 79.1% on the CPDN dataset, representing an increase of 3.9% compared to the original YOLOv9. Furthermore, it achieves an mAP@50 of 63.3% on the PASCAL VOC 2007 dataset, outperforming the original YOLOv9 by 1.6%.
Related Topics
Concepts
Computer science
Pascal (unit)
Robustness (evolution)
Upsampling
Bounding overwatch
Data mining
Fusion mechanism
Object detection
Artificial intelligence
Pattern recognition (psychology)
Fusion
Programming language
Philosophy
Image (mathematics)
Linguistics
Chemistry
Lipid bilayer fusion
Gene
Biochemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fphy.2024.1517177
- OA Status
- gold
- Cited By
- 1
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405764876
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405764876Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fphy.2024.1517177Digital Object Identifier
- Title
-
SGI-YOLOv9: an effective method for crucial components detection in the power distribution networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-24Full publication date if available
- Authors
-
Mingxu Yang, Bojian Chen, Chenxiang Lin, Wenxu Yao, Yangdi LiList of authors in order
- Landing page
-
https://doi.org/10.3389/fphy.2024.1517177Publisher 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/10.3389/fphy.2024.1517177Direct OA link when available
- Concepts
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Computer science, Pascal (unit), Robustness (evolution), Upsampling, Bounding overwatch, Data mining, Fusion mechanism, Object detection, Artificial intelligence, Pattern recognition (psychology), Fusion, Programming language, Philosophy, Image (mathematics), Linguistics, Chemistry, Lipid bilayer fusion, Gene, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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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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52Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 60, 159, 191, 212 |
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| abstract_inverted_index.The | 0, 169 |
| abstract_inverted_index.VOC | 166, 215 |
| abstract_inverted_index.and | 31, 78, 111, 136, 143, 163 |
| abstract_inverted_index.are | 157 |
| abstract_inverted_index.for | 14, 41, 51, 181 |
| abstract_inverted_index.key | 39 |
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| abstract_inverted_index.3.9% | 199 |
| abstract_inverted_index.CPDN | 161, 193 |
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| abstract_inverted_index.that | 173 |
| abstract_inverted_index.this | 45, 123 |
| abstract_inverted_index.with | 186 |
| abstract_inverted_index.1.6%. | 223 |
| abstract_inverted_index.63.3% | 211 |
| abstract_inverted_index.79.1% | 190 |
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| abstract_inverted_index.great | 12 |
| abstract_inverted_index.grid. | 22 |
| abstract_inverted_index.model | 101 |
| abstract_inverted_index.paper | 46, 124 |
| abstract_inverted_index.posed | 26 |
| abstract_inverted_index.power | 7, 21, 56 |
| abstract_inverted_index.small | 36, 83, 182 |
| abstract_inverted_index.speed | 142 |
| abstract_inverted_index.using | 104 |
| abstract_inverted_index.which | 130 |
| abstract_inverted_index.PASCAL | 165, 214 |
| abstract_inverted_index.YOLOv9 | 63, 221 |
| abstract_inverted_index.boxes. | 148 |
| abstract_inverted_index.fusion | 96 |
| abstract_inverted_index.global | 94, 102 |
| abstract_inverted_index.mAP@50 | 188, 209 |
| abstract_inverted_index.method | 50, 70 |
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| abstract_inverted_index.module | 97 |
| abstract_inverted_index.object | 183 |
| abstract_inverted_index.remain | 38 |
| abstract_inverted_index.tasks, | 185 |
| abstract_inverted_index.verify | 150 |
| abstract_inverted_index.YOLOv9. | 204 |
| abstract_inverted_index.channel | 112 |
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| abstract_inverted_index.crucial | 3, 52 |
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| abstract_inverted_index.dataset | 162 |
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| abstract_inverted_index.module. | 91 |
| abstract_inverted_index.network | 9, 58 |
| abstract_inverted_index.objects | 37, 84 |
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| abstract_inverted_index.compared | 200 |
| abstract_inverted_index.dataset, | 194, 217 |
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| abstract_inverted_index.improves | 79 |
| abstract_inverted_index.increase | 197 |
| abstract_inverted_index.original | 203, 220 |
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| abstract_inverted_index.referred | 65 |
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| abstract_inverted_index.SPDConv++ | 89 |
| abstract_inverted_index.conducted | 158 |
| abstract_inverted_index.detecting | 35 |
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| abstract_inverted_index.extensive | 155 |
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| abstract_inverted_index.Inner-PIoU | 127 |
| abstract_inverted_index.SGI-YOLOv9 | 174 |
| abstract_inverted_index.Therefore, | 44 |
| abstract_inverted_index.advantages | 133 |
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| abstract_inverted_index.difficulty | 33 |
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| abstract_inverted_index.robustness | 118 |
| abstract_inverted_index.SGI-YOLOv9, | 154 |
| abstract_inverted_index.SGI-YOLOv9. | 68 |
| abstract_inverted_index.backgrounds | 30 |
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| abstract_inverted_index.demonstrate | 172 |
| abstract_inverted_index.dimensions, | 113 |
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| abstract_inverted_index.experiments | 156 |
| abstract_inverted_index.improvement | 178 |
| abstract_inverted_index.information | 103 |
| abstract_inverted_index.introducing | 87 |
| abstract_inverted_index.significant | 177 |
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| abstract_inverted_index.environmental | 29 |
| abstract_inverted_index.outperforming | 218 |
| abstract_inverted_index.significantly | 114 |
| abstract_inverted_index.technologies. | 43 |
| abstract_inverted_index.self-attention | 106 |
| cited_by_percentile_year.max | 95 |
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| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.64456027 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |