计算机工程与应用 ›› 2017, Vol. 53 ›› Issue (15): 31-35.DOI: 10.3778/j.issn.1002-8331.1704-0438

• 热点与综述 • 上一篇    下一篇

动车底部螺栓快速定位方法

路绳方,刘  震   

  1. 北京航空航天大学 仪器科学与光电工程学院,北京 100083
  • 出版日期:2017-08-01 发布日期:2017-08-14

Fast localization method of bolt under China Railway High-Speed

LU Shengfang, LIU Zhen   

  1. School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Beijing 100083, China
  • Online:2017-08-01 Published:2017-08-14

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。

关键词: 螺栓检测, 特征提取, 图像梯度, 支持向量机, 动车

Abstract: Bolt, which lies at the bottom of China railway high-speed, is a key component of train braking system, and plays a significant role in the safe braking and running of train. The traditional locomotive maintenance mode, which is carried 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 played more and more important role in industrial field. Under the outer complex environment, the automatic inspection for bolt of CRH using image processing and analysis is full of challenges. In this paper, a method based on feature extraction and machine learning is presented, which fulfills the fast localization of bolt. Experiment result has demonstrated that the proposed algorithm is very robust to outer complex environment, especially to the changes of illumination.

Key words: bolt detection, feature extraction, image gradient, support vector machine, China Railway High-speed