抛光金属弧状面瑕疵实时检测

Real-time Detection of Arc-shaped Surface Defects of Polishing Metal

提出了一种基于机器视觉对抛光金属弧状面进行实时快速检测的方法。在离线情况下,对不同光强下的样本工件进行学习分析,构造图像的背景亮度分量与灰度水平的关系函数,提取样本工件反射分量的统计特征。在线检测时,先分析图像的灰度水平,并计算图像所对应的亮度分量;然后从图像中提取出灰度均匀的反射分量,最后对反射分量进行阈值分割并做出决策判断。实验表明,本文提出的方法能通过一次学习,适应变化的采集环境,系统具有较高的鲁棒性;检测一帧图像平均时间为40ms,准确率达98%以上,具有较高的实时性和准确性。

A new method for real-time rapid detection of arc-shape surface of polished metal based on machine vision is proposed.In the offline situation,the samples of artifacts under different light intensity are analyzed,the relation function of the background brightness component and the gray level is constructed,and the statistical characteristics of reflection component of the samples are extracted.Online testing,the image gray level and the corresponding brightness component are calculated.Then the reflection component whose gray level distribution is uniform is extracted from the image.Finally,the reflection component is processed with thresholding segmentation and decision is made.The experiments show that the proposed method can adapt to the changing light environment through one-time learning.The average time to detect an image is 40ms,and the accuracy rate is over 98%.Moreover,the system has high robustness,high real-time and high accuracy.