上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 3: 413 - 423

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.301

新型电力系统与综合能源

基于窗口自注意力网络与YOLOv5融合的输电线路通道异物检测

  • 薛昂 ,
  • 姜恩宇 ,
  • 张文涛 ,
  • 林顺富 ,
  • 米阳
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  • 上海电力大学 电气工程学院,上海 200090
薛 昂(1999—),硕士生,从事深度学习在电力系统中的应用研究.
姜恩宇,讲师;E-mail:enyu_1981@163.com.

收稿日期: 2023-07-06

  修回日期: 2023-09-17

  录用日期: 2023-11-08

  网络出版日期: 2023-11-30

基金资助

国家自然科学基金(51977127);上海市科学技术委员会资助项目(19020500800);上海市教育发展基金会和上海市教育委员会“曙光计划”(20SG52)

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Detection of Foreign Bodies in Transmission Line Channels Based on Fusion of Swin Transformer and YOLOv5

  • XUE Ang ,
  • JIANG Enyu ,
  • ZHANG Wentao ,
  • LIN Shunfu ,
  • MI Yang
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  • College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Received date: 2023-07-06

  Revised date: 2023-09-17

  Accepted date: 2023-11-08

  Online published: 2023-11-30

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摘要

针对输电线路通道异物检测背景复杂以及小目标情况下检测效果不佳等问题,提出一种基于窗口自注意力网络与YOLOv5模型融合的输电线路通道安全检测算法.首先,选用窗口自注意力(S-T)网络优化主干网络,扩大模型感受视野,增强提取有效信息的能力.其次,改进自适应空间特征融合(ASFF)模块,增强多尺度特征融合能力.最后,考虑到真实框与预测框不匹配的问题,引入结构相似性交并比(SIoU),优化边界误差,提高小目标定位准确性.实验结果表明,本文模型对线路通道多目标入侵检测精度达到90.2%,且提升了小目标检测效果;与主流目标检测算法相比,可以更好地满足输电线路通道中的异物检测需求.

关键词： 智能化巡检; 输电线路通道; 目标检测; 窗口自注意力网络; 自适应空间特征融合

本文引用格式

薛昂 , 姜恩宇 , 张文涛 , 林顺富 , 米阳 . 基于窗口自注意力网络与YOLOv5融合的输电线路通道异物检测[J]. 上海交通大学学报, 2025 , 59(3) : 413 -423 . DOI: 10.16183/j.cnki.jsjtu.2023.301

Abstract

To address the challenges of complex detection background and poor detection performance for small targets, a transmission line channel security detection algorithm based on the fusion of window self-attention network and the YOLOv5 model is proposed. First, the Swin Transformer (S-T) is employed to optimize the backbone network, expanding the perception field of the model and enhancing its ability to extract effective information. Then, the adaptive spatial feature fusion (ASFF) module is improved to enhance the feature fusion ability of the model. Finally, considering the mismatch between the real frame and the predicted frame, the structural similarity intersection over union (SIoU) is introduced to optimize the boundary errors and improve the generalization ability of the model. The experimental results show that the model proposed achieves a multi-target intrusion detection accuracy of 90.2%, and with significant improvements in the detection of small targets. This approach better meets the requirements of foreign object detection in transmission line channels compared to other object detection algorithms.

Key words： intelligent inspection; transmission line channel; object detection; Swin Transformer (S-T); adaptive spatial feature fusion (ASFF)

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