[1]贺德强,江 洲,陈基永,等.基于深度卷积神经网络的铁路接触网鸟窝检测方法研究[J].机车电传动,2019,(04):126-130.[doi:10.13890/j.issn.1000-128x.2019.04.027]
 HE Deqiang,JIANG Zhou,CHEN Jiyong,et al.Research on Detection of Bird Nests in Overhead Catenary Based on Deep Convolutional Neural Network[J].Electric Drive for Locomotives,2019,(04):126-130.[doi:10.13890/j.issn.1000-128x.2019.04.027]
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基于深度卷积神经网络的铁路接触网鸟窝检测方法研究()
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机车电传动[ISSN:1000-128X/CN:43-1125/U]

卷:
期数:
2019年04期
页码:
126-130
栏目:
试验检测
出版日期:
2019-07-10

文章信息/Info

Title:
Research on Detection of Bird Nests in Overhead Catenary Based on Deep Convolutional Neural Network
文章编号:
1000-128X(2019)04-0126-05
作者:
贺德强1江 洲1陈基永1杨严杰1姚晓阳2
(1.广西大学 机械工程学院,广西 南宁 530004; 2.中车株洲电力机车研究所有限公司,湖南 株洲 412001)
Author(s):
HE Deqiang1 JIANG Zhou1 CHEN Jiyong1 YANG Yanjie1 YAO Xiaoyang2
( 1. College of Mechanical Engineering, Guangxi University, Nanning, Guangxi 530004, China; 2. CRRC Zhuzhou Institute Co., Ltd., Zhuzhou, Huna 412001, China )
关键词:
深度学习Faster R-CNN接触网鸟窝检测卷积神经网络
Keywords:
deep learning Faster R-CNN catenary nest detection convolution neural network
分类号:
U225;TP391.9
DOI:
10.13890/j.issn.1000-128x.2019.04.027
文献标志码:
A
摘要:
鸟类在铁路接触网筑巢一直是造成接触网故障的一个重要原因,目前主要依靠人工巡检的方式确定是否存在鸟窝,不仅工作量大、漏检率高,而且效率低。因此提升接触网鸟窝的检测效率,及时排除隐患,对保障铁路安全运营具有重要的意义。针对此问题,提出了一种基于深度卷积神经网络的Faster R-CNN模型用于接触网鸟窝的自动识别。通过自定义合适的网络结构和参数,经过预训练、 RPN网络训练、Fast R-CNN网络训练以及对RPN和Fast R-CNN的联合训练,建立了适合鸟窝检测的Faster R-CNN模型,实现对鸟窝的
Abstract:
Nesting on railway catenaries by birds has been an important cause of catenary failure. At present, the inspection for judging the existence of bird’s nests is carried out artificially, which has much shortcomings in heavy working intensity, high missing-inspection rate and low detecting efficiency. So improving detecting efficiency and then removing potential hazards in time is significant to ensure the security operation of railway vehicles. Aiming at such shortcomings, a Faster R-CNN model based on deep convolution neural network was proposed to identify bird’s nests on catenaries automatically. After the steps of customizing appropriate network structure and parameters and going on pre-training, RPN network training, Fast R-CNN network training and the joint training of RPN network and Fast R-CNN network, a Faster R-CNN model which could be implemented bird’s nests detection was established. Experiment has proved that the recognizing accuracy of Faster R-CNN model is 88.5% and the recognizing time of a picture is 79 ms. Compared with the traditional HOG, DPM and convolutional neural network method, it was verify that the Faster R-CNN model based on deep convolutional neural network was efficient on detecting the existence of bird’s nests on railway catenaries.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:贺德强(1973—),男,教授,博士生导师,主要从事机车车辆、故障诊断与智能维护、网络化制造等技术的研究工作。
更新日期/Last Update: 2019-07-10