摘要
动车底部闸瓦部位的螺栓是列车制动系统中的一个重要零件,对列车的安全制动和行驶起着关键作用。对于列车零部件的维护,传统的人工检修模式显然不再适应当前铁路运输领域中高效率、高质量的检修要求。随着计算机技术和电子技术的发展,基于机器视觉的在线检测系统在工业测量领域正在发挥着越来越重要的作用。在室外复杂环境下,通过图像处理和分析的方法对螺栓进行自动检测和识别,是一种行之有效的方法,但是充满了挑战。提出了一种基于特征提取和机器学习相结合的方法,实现了螺栓的快速定位和检测。通过实验验证,提出的算法对外界复杂环境,特别是光线的变化,具有较强的鲁棒性。
Bolt,which lies at the bottom of China railway high-speed,is a key component of train braking system,andplays a significant role in the safe braking and running of train.The traditional locomotive maintenance mode,which iscarried out by trained workers,is no longer adapted to the train maintenance needs of high-efficiency and high quality.With the advancement of computer and electronic technology,the online inspection based on machine vision has playedmore and more important role in industrial field.Under the outer complex environment,the automatic inspection for boltof CRH using image processing and analysis is full of challenges.In this paper,a method based on feature extraction andmachine learning is presented,which fulfills the fast localization of bolt.Experiment result has demonstrated that theproposed algorithm is very robust to outer complex environment,especially to the changes of illumination.
作者
路绳方
刘震
LU Shengfang;LIU Zhen(School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100083, China)
出处
《计算机工程与应用》
CSCD
北大核心
2017年第15期31-35,共5页
Computer Engineering and Applications
基金
国家重大科学仪器设备开发专项(No.2012YQ140032)
科学研究与研究生培养共建项目-成果转化与产业化项目
关键词
螺栓检测
特征提取
图像梯度
支持向量机
动车
bolt detection
feature extraction
image gradient
support vector machine
China Railway High-speed
