摘要
针对零部件表面缺陷检测精度问题,提出一种基于机器视觉的零部件表面缺陷检测方法。传统的利用机器视觉对零部件表面缺陷检测方法中,由于零部件表面的光学反射特性,因此无法对零部件表面缺陷进行高精度的检测。提出的基于机器视觉的零部件表面缺陷检测方法引进了差影法检测模型,根据部件表面特征,利用分段线性灰度算法对部件表面细小的缺陷进行区域检测,并且结合了灰度共生矩阵的换算熵作为判定的依据,最终建立的缺陷检测模型是利用矩阵方位度和相似度之比进行高精度的检测。为了验证设计的基于机器视觉的零部件表面缺陷检测方法的有效性,通过仿真试验证明了该设计方法,结果表明该方法能够有效地解决零部件表面缺陷检测的精度问题。
In allusion to the precision problem of parts surface defect detection, a machine vision based detection method for defects on parts surface is proposed. For the traditional parts surface defect detection method based on machine vision, it is not possible to perform high-precision parts surface defect detection due to the optical reflection characteristic of parts surface. The difference image detection model is introduced in the proposed parts surface defect detection method based on machine vi- sion. Aceording to the features of parts surface, the piecewise linear gray algorithm is adopted to perform area detection for small defects on parts surface, and with the conversion entropy of the gray level co-occurrence matrix as the judging basis, the finally-constructed defect detection model utilizes the ratio of matrix orientation degree to similarity degree to perform high-preci- sion detection. In order to verify the validation of the designed parts surfaee defect detection method based on machine vision, the simulation experiment was carried out. The experimental results show that the designed method can effectively resolve the precision problem of parts surface defect detection.
出处
《现代电子技术》
北大核心
2017年第24期181-183,共3页
Modern Electronics Technique
基金
国家自然科学基金(61302124)
江苏省科技计划项目(BY2016030-20)
常州市科技支撑计划(工业)项目(CE20150014)
关键词
机器视觉
零部件表面缺陷
差影法
灰度计算
machine vision
parts surface defect
difference image method
gray calculation
