SGI-YOLOv9: an effective method for crucial components detection in the power distribution network
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 technolo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1517177/full |
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| author | Mianfang Yang Bojian Chen Chenxiang Lin Wenxu Yao Yangdi Li |
| author_facet | Mianfang Yang Bojian Chen Chenxiang Lin Wenxu Yao Yangdi Li |
| author_sort | Mianfang Yang |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-d1aa7499d5164e03ad8ebf7a6eacd709 |
| institution | OA Journals |
| issn | 2296-424X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Physics |
| spelling | doaj-art-d1aa7499d5164e03ad8ebf7a6eacd7092025-08-20T02:31:56ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-12-011210.3389/fphy.2024.15171771517177SGI-YOLOv9: an effective method for crucial components detection in the power distribution networkMianfang YangBojian ChenChenxiang LinWenxu YaoYangdi LiThe 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%.https://www.frontiersin.org/articles/10.3389/fphy.2024.1517177/fullcrucial componentsmart gridattention mechanismYOLOv9deep learning |
| spellingShingle | Mianfang Yang Bojian Chen Chenxiang Lin Wenxu Yao Yangdi Li SGI-YOLOv9: an effective method for crucial components detection in the power distribution network Frontiers in Physics crucial component smart grid attention mechanism YOLOv9 deep learning |
| title | SGI-YOLOv9: an effective method for crucial components detection in the power distribution network |
| title_full | SGI-YOLOv9: an effective method for crucial components detection in the power distribution network |
| title_fullStr | SGI-YOLOv9: an effective method for crucial components detection in the power distribution network |
| title_full_unstemmed | SGI-YOLOv9: an effective method for crucial components detection in the power distribution network |
| title_short | SGI-YOLOv9: an effective method for crucial components detection in the power distribution network |
| title_sort | sgi yolov9 an effective method for crucial components detection in the power distribution network |
| topic | crucial component smart grid attention mechanism YOLOv9 deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1517177/full |
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