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Research on the size of mechanical parts based on image recognition

机译:基于图像识别的机械零件尺寸研究

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Because the image measurement technology based on machine vision has the advantages of high accuracy, high efficiency and non-contact measurement, this kind of measurement technology has gradually become the focus of attention in industrial production measurement and detection. Based on the analysis of image measurement technology, this paper studies the measurement method of mechanical parts size based on image recognition and improves related algorithms. Specific research work is as follows: Design a measurement method of machine parts size based on image recognition, study and analyze the formation of noise, types and corresponding denoising technology, select a fast median filtering algorithm to achieve filtering. Polynomial interpolation is applied to the sub-pixel edge location method to extract the edges accurately. Some classical operators are studied and analyzed with the specific part image to be tested as the experimental object. Several classical operators are compared and analyzed through many experiments. Experiments show that the improved morphological gradient operator can effectively refine the image edge. The experimental scheme proposed in this paper can better realize the measurement of mechanical parts size, and the improved algorithm has significantly improved the accuracy than before. (C) 2019 Published by Elsevier Inc.
机译:由于基于机器视觉的图像测量技术具有高精度,高效率和非接触式测量的优点,因此这种测量技术逐渐成为工业生产测量和检测中关注的焦点。基于图像测量技术的分析,本文研究了基于图像识别的机械部件尺寸的测量方法,提高了相关算法。具体研究工作如下:设计基于图像识别,研究和分析噪声,类型和相应的去噪技术的形成的机器零件的测量方法,选择快速中值过滤算法来实现滤波。将多项式插值应用于子像素边缘定位方法,精确地提取边缘。研究并分析了一些经典的操作员,并用特定部分图像进行测试作为实验对象。通过许多实验进行比较和分析几种古典算子。实验表明,改进的形态梯度算子可以有效地改进图像边缘。本文提出的实验方案可以更好地实现机械部件尺寸的测量,并且改进的算法显着提高了比以前的准确性。 (c)2019年由elsevier公司发布

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