湖北电力2024，Vol.48Issue(2)：121-127,7.DOI:10.19308/j.hep.2024.02.017
一种变电站设备表计缺陷图像识别方法
A Novel Image Recognition Method for Substation Meter Device Defects
陈永昕 1杜镇安 2黎恒烜 3张侃君 3姚伟 4龙昌武 5滕捷3
作者信息
- 1. 强电磁工程与新技术国家重点实验室(华中科技大学),湖北 武汉 430074||国网湖北省电力有限公司电力科学研究院,湖北 武汉 430077
- 2. 国网湖北省电力有限公司,湖北 武汉 430048
- 3. 国网湖北省电力有限公司电力科学研究院,湖北 武汉 430077
- 4. 强电磁工程与新技术国家重点实验室(华中科技大学),湖北 武汉 430074
- 5. 国网湖北省电力有限公司黄石供电公司,湖北 黄石 435000
- 折叠
摘要
Abstract
With the construction and application of a new-generation substation centralized monitoring system,the collection of massive inspection data has accelerated the application of artificial intelligence(AI)in the field of equipment management and control.Automatic and rapid identification of substation equipment defects is of vital importance for the construction of a new mode of"unattended+centralized monitoring"substations'operation and maintenance.Therefore,this paper proposes an improved YOLOv9-based image recognition method:an efficient multi-scale attention(EMA)mechanism based on cross-space learning is introduced into the backbone network layer of YOLOv9 to extract key features of small and medium-sized targets in meter images of substation equipment.Meanwhile,Inner-SIoU bounding box regression loss function is used to improve the convergence and accuracy of YOLOv9.The experimental results show that the precision rate(P),recall rate(R),and mean average precision(mAP)of the proposed method are superior to those of the baseline method.关键词
变电站/无人值守/集中监控/图像识别/表计/YOLOv9/注意力机制/边界框回归损失函数Key words
substation/unattended/centralized supervisory control/image recognition/meter device/YOLOv9/attention mechanism/bounding box regression loss function分类
动力与电气工程引用本文复制引用
陈永昕,杜镇安,黎恒烜,张侃君,姚伟,龙昌武,滕捷..一种变电站设备表计缺陷图像识别方法[J].湖北电力,2024,48(2):121-127,7.
