基于改进yolov4-tiny的地铁受电弓燃弧检测方法

艾生永; 丁建明; 张青松

doi:10.13890/j.issn.1000-128X.2023.04.012

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基于改进yolov4-tiny的地铁受电弓燃弧检测方法

铁道电气化 | 更新时间：2024-08-02

- 基于改进yolov4-tiny的地铁受电弓燃弧检测方法
- Arcing detection method of metro pantograph based on improved yolov4-tiny
- 机车电传动 2023年第4期页码：83-89
- 作者机构：
  
  西南交通大学轨道交通运载系统全国重点实验室，四川成都 610031
- 作者简介：
  
  丁建明（1981—），男，博士，副研究员，博士生导师，主要从事信号处理、故障诊断、智能运维方面的研究； E-mail: fdingjianming@126.com
- 基金信息：
  
  国家自然科学基金项目(51875481)
- DOI：10.13890/j.issn.1000-128X.2023.04.012
  中图分类号： U231;U225
- 纸质出版日期：2023-07-10，
  
  收稿日期：2023-03-07，
  
  修回日期：2023-07-01，
- 稿件说明：
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艾生永, 丁建明, 张青松. 基于改进yolov4-tiny的地铁受电弓燃弧检测方法[J]. 机车电传动, 2023(4): 83-89.

AI Shengyong, DING Jianming, ZHANG Qingsong. Arcing detection method of metro pantograph based on improved yolov4-tiny[J]. Electric drive for locomotives,2023(4): 83-89.
艾生永, 丁建明, 张青松. 基于改进yolov4-tiny的地铁受电弓燃弧检测方法[J]. 机车电传动, 2023(4): 83-89. DOI： 10.13890/j.issn.1000-128X.2023.04.012.

AI Shengyong, DING Jianming, ZHANG Qingsong. Arcing detection method of metro pantograph based on improved yolov4-tiny[J]. Electric drive for locomotives,2023(4): 83-89. DOI： 10.13890/j.issn.1000-128X.2023.04.012.

摘要

地铁受电弓的燃弧会加剧受电弓滑板和接触网磨损，严重危害轨道交通安全。文章针对地铁车辆受电弓燃弧检测存在强光干扰和背景多变的问题，提出了一种基于yolov4-tiny模型改进的燃弧检测方法。为提升小目标检测能力，该方法在yolov4-tiny原有两个尺度的预测分支的基础上，添加第三尺度的预测分支以实现小燃弧在浅层网络中的定位，在主干网络后增加RFB(Receptive Field Block)模块以扩大网络的感受野，增强模型的特征提取能力。结果表明，改进的模型在测试集上的平均精度值（

）比yolov4-tiny提升了7.8个百分点，达到了98.2%，燃弧的定位效果与yolov4相当，但速度得到了极大的提升，单张图片的推理速度仅为6.5 ms，能有效、准确地完成地铁车辆中的受电弓燃弧检测任务。

Abstract

Arcing on metro pantographs contribute to the accelerated wear of pantograph strips and the catenary

imposing a serious safety risk to rail transit. For the problem of strong light interference and variable background in pantograph arcing detection for metro vehicles

an arcing detection method based on an improved yolov4-tiny model is proposed. In order to improve the detection ability of small targets

this method incorporated a third-scale prediction branch to the original two-scale prediction branches of yolov4-tiny

to enable the positioning of small arcs in the shallow network. Moreover

a receptive field block (RFB) module was embedded beneath the backbone network

to expand the network′s receptive field and enhance the model′s feature extraction ability. The test results show the average precision (

) of the improved model on the test set is increased to 98.2%

marking an improvement of 7.8 percentage points over yolov4-tiny. Despite yielding similar positioning effect for arcs

the improved model works at significantly higher speeds than yolov4

enabling inference speed of a single image in only 6.5 ms. The proposed

method is proven effective and accurate in fulfilling the arcing detection task for pantographs of metro vehicles.

关键词

地铁车辆燃弧识别目标检测YOLO深度学习

Keywords

metro vehiclearcing detectiontarget detectionYOLOdeep learning

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