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TECHNOLOGY AND CODE article

Front. Phys.
Sec. Radiation Detectors and Imaging
Volume 12 - 2024 | doi: 10.3389/fphy.2024.1517177
This article is part of the Research Topic Multi-Sensor Imaging and Fusion: Methods, Evaluations, and Applications, Volume III View all articles

SGI-YOLOv9: An Effective method for Crucial Components Detection in the Power Distribution Network

Provisionally accepted
Mianfang Yang Mianfang Yang Bojian Chen Bojian Chen *Chenxiang Lin Chenxiang Lin Wenxu Yao Wenxu Yao Yangdi Li Yangdi Li
  • State Grid Fujian Electric Power Research Institute, Fuzhou, China

The final, formatted version of the article will be published soon.

    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%.

    Keywords: Crucial component, Smart Grid, attention mechanism, YOLOv9, deep learning

    Received: 25 Oct 2024; Accepted: 21 Nov 2024.

    Copyright: © 2024 Yang, Chen, Lin, Yao and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Bojian Chen, State Grid Fujian Electric Power Research Institute, Fuzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.