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.
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第11期64-69,共6页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金重点资助项目(60835004)
关键词
机器视觉
铁轨
表面缺陷
图像增强
自动阈值分割
machine vision
rail
surface defects
image enhancement
automatic threshold segmentation
