摘要
针对轴承钢球表面缺陷高精度高效率的检测要求,提出一种检测算法.首先提出一种圆形轮廓外接矩形重绘算法,将目标钢球区域与背景区域分离,获取感兴趣区域;然后利用图像差分法将待检钢球图像与标准图像相减,获取差值图像;最后对差值图像中钢球中间环带区域二值化处理并提取缺陷特征.基于VC++6.0开发环境、Open CV计算机视觉库实验验证并评价算法性能.结果表明:该算法适用于多种钢球缺陷,处理单幅图像的平均耗时小于30 ms,缺陷识别率超过97%,误检率小于3%,针对直径为5.953 mm钢球的检测效率可达12 000个/h,满足工业检测要求,具有良好的应用前景.
According to the detection demand of bearing steel ball surface defects with high precision and efficiency, a detection algorithm is proposed. To begin with, the algorithm of redrawing external rectangles of rounded contours was applied to segregate the area of targeted steel ball from the background area so as to pick up regions of interest; then, with image subtraction, a difference image could be got by subtraction between images of regions of interest and standard images; finally, binary the band area right in the middle of the ball for defect features. Experiment and evaluation on algorithm performances based on VC＋＋ 6.0 development environment and Open CV computer vision library demonstrate that the algorithm is suitable for various defects, with the average processing time less than 30 ms for a single image, the recognition rate more than 97%, the mistake examining rate lower than 3% and the detection efficiency as high as 12 000 per hour for the diameter of 5.953 mm. Thus, it meets the demand of industrial assembly, enjoying a sound application prospect.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2016年第10期1699-1704,共6页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金(51275140)
关键词
钢球
机器视觉
缺陷检测
外接矩形
图像差分法
steel ball
machine vision
defect detection
external rectangle
image subtraction
