基于计算机视觉的铜金属杂质检测方法

Method for detecting copper metal impurities based on computer vision

计算机视觉图像处理技术被采用,对金属铜中的所含杂质种类及含量进行检测。基于传统的检测方法,我们提出了一种新的检测技术,该方法在检测精度上得到了很大地提升。新方法是以局部轮廓的特征为基准,提取金属铜中的杂质并且对杂质做定性以及定量分析。首先对采集完的金属铜图像实施局部轮廓视觉特征的提取,然后运用小波技术对采集完成的图像进行阈值滤波,最后Harris角点检测方法被采用来对金属铜中的杂质做定点成分标记,从而使图像的杂质噪点检测得到进一步优化,提高检测的准确率。仿真实验的结果表明,采用本文中的方法对铜金属中杂质进行检测,检测准确率较高,处理后的图像具有更好的平滑性。

Computer vision image processing technology is adopted to detect the impurity and content of metal copper. Based on the traditional detection method, we propose a new detection technology, which has greatly improved the detection accuracy. The new method is based on the characteristics of local contour, extract the impurities in metal copper and make qualitative and quantitative analysis of impurities. The implementation of local visual features extracted contour of copper after image acquisition, then uses wavelet threshold filtering technology of image acquisition is completed, the Harris corner detection method is adopted to do fixed-point component labeling of impurities in copper metal, so that impurities noise detection image has been further optimized to improve the accuracy of detection. The. Simulation results show that the method used in this paper to detect impurities in copper metal, the detection accuracy is high, the processed image has better smoothness.