作　　者： ; ; (贾康成）;

机构地区： 湖南大学电气与信息工程学院

出　　处： 《湖南大学学报（自然科学版）》 2013年第11期64-69,共6页

摘　　要： 基于线扫描的机器视觉成像系统,用于采集铁轨表面图像,提出一种以图像增强和自动阈值分割为核心的缺陷检测算法,该算法能够准确检测出铁轨表面缺陷.图像增强采用局部零均值法,克服了铁轨表面光线反射不均的缺点,提高了缺陷和背景的区分度.自动阈值分割采用强调概率的最大背景类方差法,取到的阈值使背景类方差最大的同时保持缺陷出现概率较小.将本文的核心方法与传统方法进行对比实验,验证了该算法的有效性和快速性,具有一定的实用价值. This paper introduced a machine vision imaging system to acquire rail surface images based on line scanning,and presented an algorithm to detect rail surface defects accurately based on image enhancement and automatic thresholding.We proposed a local zero mean measure to enhance rail images,which can overcome the nonuniform reflection of the rail surface and improve the distinction between defects and background.And then,we put forward a proportion emphasizing maximum background-class variance measure to select a threshold,which maximizes the background-class variance and meanwhile keeps the defect proportion in a low level.Through experiments,we compared the core of the algorithm with well-established methods,and then proved the validity and rapidity of the algorithm with wide applicability.

关 键 词： 机器视觉 铁轨 表面缺陷 图像增强 自动阈值分割

领　　域： [交通运输工程]