基于深度学习的无人机航拍输电线路侵入异物检测综述

刘传洋; 吴一全; 刘景景

doi:10.11959/j.issn.2096-6652.ZNKX26042

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基于深度学习的无人机航拍输电线路侵入异物检测综述

综述与展望 | 更新时间：2026-07-29

- 基于深度学习的无人机航拍输电线路侵入异物检测综述
- Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography
- 智能科学与技术学报 2026年8卷第2期页码：149-168
- 作者机构：
  
  1.南京航空航天大学电子信息工程学院，江苏南京 211106
  2.池州学院机电工程学院，安徽池州 247000
- 作者简介：
  
  刘传洋（1982- ），男，南京航空航天大学电子信息工程学院博士生，池州学院机电工程学院副教授，主要研究方向为图像处理与机器视觉检测。
  吴一全（1963- ），男，南京航空航天大学电子信息工程学院教授、博士生导师，主要研究方向为视觉检测与图像测量、遥感图像处理与理解、红外目标检测与识别、视频处理与智能分析。nuaaimage@163.com
  刘景景（1981- ），女，池州学院机电工程学院副教授，主要研究方向为图像处理与机器视觉检测。
- 基金信息：
  
  国家自然科学基金资助项目(61573183);安徽省高校自然科学研究重点项目(2025AHGXZK30826;KYJG002)
- DOI：10.11959/j.issn.2096-6652.ZNKX26042
  中图分类号： TP391.41;TM930.1
- 收稿：2026-04-07，
  
  修回：2026-06-11，
  
  录用：2026-06-27，
  
  网络首发：2026-07-27，
  
  纸质出版：2026-06-15
- 稿件说明：
移动端阅览
刘传洋,吴一全,刘景景.基于深度学习的无人机航拍输电线路侵入异物检测综述[J].智能科学与技术学报,2026,08(02):149-168.

Liu Chuanyang,Wu Yiquan,Liu Jingjing.Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):149-168.
刘传洋,吴一全,刘景景.基于深度学习的无人机航拍输电线路侵入异物检测综述[J].智能科学与技术学报,2026,08(02):149-168. DOI： 10.11959/j.issn.2096-6652.ZNKX26042.

Liu Chuanyang,Wu Yiquan,Liu Jingjing.Review of deep learning-based foreign object intrusion detection for transmission lines via UAV aerial photography[J].Chinese Journal of Intelligent Science and Technology,2026,08(02):149-168. DOI： 10.11959/j.issn.2096-6652.ZNKX26042.

摘要

基于无人机航拍图像和深度学习算法实现输电线路侵入异物的精准有效检测，有助于快速排查输电线路潜在安全隐患，进而提升电网精益化运维能力。综述了基于深度学习的无人机航拍输电线路侵入异物检测方法的研究进展。首先，从Faster R-CNN、SSD、YOLO系列算法及其他衍生算法4个维度，系统梳理了基于深度学习的无人机航拍图像异物检测方法。其次，阐释了无人机航拍图像下输电线路侵入异物检测的改进模型，分别阐明了注意力机制、多尺度特征融合、轻量化网络等技术在输电线路侵入异物检测中的应用路径及其带来的性能优化效果。接着，剖析了基于传统Transformer架构的DETR类异物检测模型，以及面向电力行业的Transformer大模型及其工程应用。最后，针对现有无人机航拍输电线路侵入异物智能检测面临的挑战，对下一步研究工作进行了展望。

Abstract

Efficient and accurate detection of foreign object intrusion on transmission lines based on UAV aerial images and deep learning algorithms enables rapid identification of potential safety hazards

thereby improving the lean operation and maintenance capability of power grids. The research progress of deep learning-based foreign object intrusion detection methods for transmission lines using UAV aerial images was reviewed. First

deep learning-based foreign object detection methods for UAV aerial images were systematically reviewed from four dimensions: Faster R-CNN

SSD

the YOLO series algorithms

and other derivative algorithms. Second

an improved model for UAV aerial image-based foreign object intrusion detection in transmission lines was elaborated. It illustrated the application strategies and performance optimization effects of core technologies in detection tasks

including attention mechanisms

multi-scale feature fusion

and lightweight networks. Subsequently

the DETR-based foreign object detection models built on the vanilla Transformer architecture were investigated

as well as large Transformer models tailored for the power industry and their engineering applications. Finally

in view of the current challenges in UAV aerial image-based intelligent foreign object detection for transmission lines

future research prospects were proposed.

关键词

Keywords

references

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