基于AAGV-YOLOX模型的输电线路异物检测研究

谢国波; 夏炜; 林志毅; 谢建辉; 刘汉林; 余意

doi:10.12052/gdutxb.240174

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基于AAGV-YOLOX模型的输电线路异物检测研究

电气工程 | 更新时间：2026-05-27

- 基于AAGV-YOLOX模型的输电线路异物检测研究
- Research on Foreign Object Detection in Power Transmission Lines Based on the AAGV-YOLOX Model
- 广东工业大学学报 2026年43卷第2期页码：81-90
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  谢国波(1977–) ，男，教授，博士，主要研究方向为遥感大数据、深度学习，E-mail：guoboxie@163.com
  林志毅(1979–) ，男，讲师，博士，主要研究方向为人工智能和生物信息学等，E-mail：lzy291@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(62076073)
- DOI：10.12052/gdutxb.240174
  中图分类号： TP391.41;TM75
- 纸质出版：2026
- 稿件说明：
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谢国波, 夏炜, 林志毅, 等. 基于AAGV-YOLOX模型的输电线路异物检测研究[J]. 广东工业大学学报, 2026, 43(2): 81-90.
谢国波, 夏炜, 林志毅, 等. 基于AAGV-YOLOX模型的输电线路异物检测研究[J]. 广东工业大学学报, 2026, 43(2): 81-90. DOI： 10.12052/gdutxb.240174.

DOI： 10.12052/gdutxb.240174

摘要

鉴于目前输电线路异物检测过程中容易出现的错检、漏检问题，以及异物尺度多变导致检测精度有限的挑战，本文提出了一种用于输电线路异物检测的AAGV-YOLOX模型。该模型首先设计了自适应扩张卷积(Adaptive Dilated Convolution

ADConv)并构建了特征提取模块(Adaptive Dilated Convolution Module

ADCM)，有效区分了广泛分布场景中的异物和背景信息，提高了模型的特征提取能力。接着，在颈部网络中引入了自适应感受野特征融合(Adaptive Receptive Field Feature Fusion

ARFFF)模块，使模型能够充分融合不同尺度的特征，进一步提升了检测精度。最后提出了GVFL损失函数，不仅提高了网络的收敛速度，还增强了定位精度。实验结果表明，本模型在自建的输电线路异物数据集上的平均精度均值达到了90.34%，相较于YOLOXs提升了5.56个百分点，证明了所提出方法在提升输电线路异物检测效果方面的有效性。

Abstract

To address the common issues of false and missed detections in the current process of foreign object detection on power transmission lines

as well as the limited detection accuracy due to the variability in the size of foreign objects

this paper proposes an AAGV-YOLOX model for the detection of foreign objects on power transmission lines. The model first designs an Adaptive Dilated Convolution (Adaptive Dilated Convolution

ADConv) and constructs a feature extraction module (Adaptive Dilated Convolution Module

ADCM) to effectively distinguish the widely distributed foreign objects from background information

thereby enhancing the model's feature extraction capabilities. Subsequently

an Adaptive Receptive Field Feature Fusion (Adaptive Receptive Field Feature Fusion

ARFFF) module is introduced into the neck network to fully integrate features of different scales

further improving detection accuracy. Finally

the GVFL loss function is proposed

which not only increases the convergence speed of the proposed network but also enhances the localization accuracy. Experimental results show that the average precision mean of this model on the self-built dataset of foreign objects on power transmission lines reaches 90.34%

with a 5.56 percentage points improvement over the YOLOXs

demonstrating the effectiveness of the proposed method in improving the detection of foreign objects on power transmission lines.

关键词

Keywords

references

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