摘要
采用直观、高效的基于机器视觉的检测方式对生产线冷态重轨表面缺陷进行自动化检测.以彩色双目线阵相机作为采集传感器获取深度信息和RGB信息.深度信息用于缺陷快速检测,RGB信息及深度信息用于缺陷分割.然后,提出一个基于一维卷积网络的深度网络用于缺陷快速检测.该网络主要包括基于一维卷积网络的特征提取器,由全连接层和Dropout层组成的分类器,以及加入尺寸先验的滤波器.为了验证所提出的网络性能,本文搭建了数据采集平台并对重轨样件进行了数据采集.实验结果表明,本文的快速检测网络在采集的数据上缺陷级检测率为100%,误检率为35%,优于对比网络.
An intuitive and efficient method based on machine vision was applied to the automatic detection of cold heavy rail surface defects of production line.Color binocular linear scan camera gathered the depth information and RGB information.Depth information was employed for the rapid detection of defects,and for defect segmentation combined with RGB information.Then a deep learning network was proposed for the rapid detection of defects.The network mainly includes a feature extractor based on one-dimensional convolution network,a classifier composed of full connection layers and dropout layers,and a filter with size prior.Finally,a data acquisition platform was setup and the data of heavy rail samples were collected for the verification of network performance.The results show that the network proposed performs well.The defect-level detection rate is 100%and the false detection rate is 35%on the collected data,which is better than that of the compared networks.
作者
张德富
宋克臣
牛孟辉
颜云辉
ZHANG De-fu;SONG Ke-chen;NIU Meng-hui;YAN Yun-hui(School of Mechanical Engineering&Automation,Northeastern University,Shenyang 110819,China.)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第2期276-281,共6页
Journal of Northeastern University(Natural Science)
基金
国家重点研发计划项目(2017YFB0304200)
国家自然科学基金资助项目(51805078)
中央高校基本科研业务费专项资金资助项目(N2003021).
关键词
生产线冷态重轨
表面缺陷
机器视觉
深度信息
一维卷积网络
cold heavy rail of production line
surface defect
machine vision
depth information
one-dimensional convolution network
