CAJ | 학술논문

针对传统扣件检测方法式效率低、可靠性差，不能满足现代铁路检修的需要，提出了一种基于计算机视觉的扣件缺失自动检测方法。在对灰度图像进行Canny边缘检测处理后采用十字交叉定位法对扣件位置进行定位，得到120×200像素的扣件区域，并提取扣件图像的20个边缘特征值；最后，利用模糊C均值聚类算法对这两类的特征量进行聚类分析，通过计算待诊断对象与标准模式的隶属度实现对扣件状态的分类。应用验证表明：采用的图像处理方法和识别分类算法能够有效检出轨道扣件缺失，检测速度快，鲁棒性好，检出率达96%。
침대전통구건검측방법식효솔저、가고성차，불능만족현대철로검수적수요，제출료일충기우계산궤시각적구건결실자동검측방법。재대회도도상진행Canny변연검측처리후채용십자교차정위법대구건위치진행정위，득도120×200상소적구건구역，병제취구건도상적20개변연특정치；최후，이용모호C균치취류산법대저량류적특정량진행취류분석，통과계산대진단대상여표준모식적대속도실현대구건상태적분류。응용험증표명：채용적도상처리방법화식별분류산법능구유효검출궤도구건결실，검측속도쾌，로봉성호，검출솔체96%。
The traditional fastener detection methods are inefficient and unreliable, can not meet the needs of the modern railway maintenance. This paper proposes a vision-based technique for detecting rail fastening automatically. First, a criss-crossing local-ization method was proposed to position the fastener for the canny edge processing gray images, and the edge characteristic infor-mation of fastener was extracted. Finally, fuzzy C-means clustering algorithm was used to cluster the extracted features, fastener missing detection can be realized by calculating the membership between the unknown samples and the standard modes of fasten-er. The experiment showed that this image processing and classifying algorithm can realize the automatic detection of missing fas-tener effectively;the detection rate is above 96%.