基于多尺度特征融合的变电站设备缺陷检测

邢壮; 温书凯

doi:10.11959/j.issn.1000-0801.DXKX260174

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基于多尺度特征融合的变电站设备缺陷检测

更新时间：2026-06-24

- 基于多尺度特征融合的变电站设备缺陷检测
- Substation Equipment Fault Detection Based on Multi-scale Feature Fusion
- 电信科学 2026年
- 作者机构：
  
  1.西安大麦网络科技有限公司，陕西西安 710016
  2.西安交通大学，电气工程学院，陕西西安 710049
- 作者简介：
  
  邢壮，1991，男，西安大麦网络科技有限公司，总经理，副高，主要从事电力设备故障诊断方面的研究。
  温书凯，1987，男，西安交通大学电气工程学院，教授，主要从事计算机应用、电力设备故障诊断技术方面的研究。
- 基金信息：
  
  国家自然科学基金(12301559)
- DOI：10.11959/j.issn.1000-0801.DXKX260174
  中图分类号： TP391.4;TM63
- 收稿：2026-03-17，
  
  修回：2026-05-13，
  
  录用：2026-06-01，
- 稿件说明：
移动端阅览
邢壮, 温书凯. 基于多尺度特征融合的变电站设备缺陷检测[J/OL]. 电信科学, 2026.

Xing Zhuang, Wen Shukai. Substation Equipment Fault Detection Based on Multi-scale Feature Fusion[J/OL]. Telecommunications Science, 2026.
邢壮, 温书凯. 基于多尺度特征融合的变电站设备缺陷检测[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260174.

Xing Zhuang, Wen Shukai. Substation Equipment Fault Detection Based on Multi-scale Feature Fusion[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260174.

摘要

针对变电站设备缺陷目标尺度跨度大及背景干扰严重等问题，提出一种基于多尺度特征融合的变电站设备缺陷检测模型（Substation Equipment Defect Detection Transformer

SED-DETR）。模型在编码阶段设计分组级联自注意力模块（Grouped Cascaded Self-Attention

GCSA），增强对关键区域的聚焦能力；在颈部阶段构建跨阶段多分支特征融合网络（Cross-Scale Multi-Branch Fusion Feed-Forward Network

CSMB-FFN），通过改进单位步长融合卷积（Unit Step Fusion Convolution

US-Conv）实现多尺度特征精准对齐，引入跨阶段多分支建模模块（Cross-Stage Partial Omni-Kernel Block

CSP-OKB）加强全局感知与局部纹理信息融合；训练阶段采用MPDIoU（Minimum Pairwise Distance Intersection over Union）替换边界框回归损失中的GIoU（Generalized Intersection over Union），增强高重叠样本条件下的回归监督敏感性。SED-DETR在自建数据集和IGV Dataset上均取得优于当前主流检测模型的检测结果，mAP@0.5分别为94.1%和95.4%，mAP@0.5:0.95分别达到77.6%和85.2%。端侧部署结果说明，该模型在Jetson Orin Nano 4GB平台上具备良好的实时推理能力，经FP16加速后，推理时间缩短至23.9ms，且mAP@0.5达到92.5%。结果表明，SED-DETR在保证较高检测精度的同时兼顾计算效率，为变电站设备缺陷检测任务提供可行方案。

Abstract

To address the large-scale variation of defect targets and severe background interference in substation equipment inspection images

this paper proposes a substation equipment defect detection model

termed the Substation Equipment Defect Detection Transformer (SED-DETR)

based on multi-scale feature fusion. In the encoder

a Grouped Cascaded Self-Attention (GCSA) module is introduced to enhance focus on critical regions and improve cross-region contextual modeling. In the neck

a Cross-Scale Multi-Branch Fusion Feed-Forward Network (CSMB-FFN) is constructed

where an improved Unit Step Fusion Convolution (US-Conv) is employed for precise multi-scale feature alignment

and a Cross-Stage Partial Omni-Kernel Block (CSP-OKB) module is incorporated to strengthen the fusion of global context and local texture information. During training

the Generalized Intersection over Union (GIoU) term in the bounding-box regression loss is replaced with the Minimum Pairwise Distance Intersection over Union (MPDIoU) to improve the sensitivity of regression supervision under highly overlapped samples. Experimental results show that SED-DETR achieves mAP@0.5 values of 94.1% and 95.4%

and mAP@0.5:0.95 values of 77.6% and 85.2%

on the self-collected dataset and the public IGV Dataset

respectively

outperforming current mainstream detection models. Edge-side deployment results further demonstrate that the proposed model maintains favorable real-time inference capability on the Jetson Orin Nano 4GB platform; after FP16 acceleration

the inference time is reduced to 23.9 ms while the mAP@0.5 remains 92.5%. These results indicate that SED-DETR achieves a favorable balance between detection accuracy and computational efficiency

providing a practical solution for substation equipment defect detection.

关键词

Keywords

references

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