本文選題：攝像機(jī)標(biāo)定 + 軸徑　； 參考：《吉林大學(xué)》2017年碩士論文

【摘要】：在精密測(cè)試技術(shù)領(lǐng)域,機(jī)器視覺測(cè)量技術(shù)是一項(xiàng)新興技術(shù),該技術(shù)可以實(shí)現(xiàn)零件的尺寸測(cè)量、目標(biāo)的視覺跟蹤及機(jī)械裝置的運(yùn)動(dòng)分析等功能。機(jī)器視覺測(cè)量技術(shù)利用計(jì)算機(jī)代替“人眼”,看到圖像并理解圖像,對(duì)攝像機(jī)采集的數(shù)字圖像信息特征進(jìn)行提取,最終完成視覺檢測(cè)。軸類零件是各類機(jī)械裝置中應(yīng)用最廣泛的一種零件,隨著技術(shù)的探索與進(jìn)步,對(duì)機(jī)械裝置的精密度以及運(yùn)轉(zhuǎn)速度的要求越來(lái)越高,在機(jī)加工過程中常常需要對(duì)軸類零件進(jìn)行高精度的加工,因此對(duì)軸類零件直徑尺寸的準(zhǔn)確測(cè)量顯得尤為重要。本文根據(jù)軸類零件的自身幾何特征及光學(xué)成像過程,提出一種軸徑測(cè)量方法。首先,本文根據(jù)小孔成像原理建立攝像機(jī)的成像模型,分析了攝像機(jī)標(biāo)定技術(shù)�；趶堈训钠矫鏄�(biāo)定法,在畸變模型中考慮兩項(xiàng)徑向畸變和兩項(xiàng)切向畸變,以此修正光學(xué)鏡頭畸變,進(jìn)而提高了攝像機(jī)內(nèi)部參數(shù)和外部參數(shù)的標(biāo)定精度;為提高攝像機(jī)參數(shù)標(biāo)定精度,通過攝像機(jī)標(biāo)定實(shí)驗(yàn),確定合適的角點(diǎn)檢測(cè)數(shù)目以及背光源光照強(qiáng)度。然后,為提高圖像測(cè)量的檢測(cè)精度,從圖像中獲取被測(cè)物體的亞像素邊緣位置是至關(guān)重要的。本文首先理論分析了幾類常見的亞像素邊緣檢測(cè)方法,包括灰度矩法、空間矩法、Zernike正交矩法、高斯擬合法、雙曲正切擬合法、梯度插值法以及三點(diǎn)插值法;最后以實(shí)際測(cè)量尺寸的誤差作為亞像素邊緣檢測(cè)精度的評(píng)價(jià)準(zhǔn)則,通過測(cè)量實(shí)驗(yàn),確定了穩(wěn)定性較好并且檢測(cè)精度較高的梯度插值法,作為本文軸徑測(cè)量的亞像素邊緣檢測(cè)方法。接下來(lái),本文首先根據(jù)軸零件在攝像機(jī)下的成像過程,建立軸徑測(cè)量的數(shù)學(xué)模型,并著重減小了軸線與圖像平面不平行所帶來(lái)的誤差;然后對(duì)軸徑測(cè)量數(shù)學(xué)模型中的未知參數(shù)進(jìn)行標(biāo)定;最后,考慮到測(cè)量軸徑的實(shí)用性與便捷性,提出了一種基于目標(biāo)軸的真實(shí)軸徑與像素軸徑比值的標(biāo)定系數(shù)方法。最后,通過軸徑測(cè)量實(shí)驗(yàn),驗(yàn)證本文提出的軸徑測(cè)量方法的精度及標(biāo)定系數(shù)方法的實(shí)用性。對(duì)兩種軸徑測(cè)量方法的測(cè)量結(jié)果進(jìn)行比較,并分析了影響測(cè)量精度的誤差因素,為提高測(cè)量精度確定方向。對(duì)于機(jī)器視覺測(cè)量技術(shù)的發(fā)展來(lái)說,本文的研究工作有一定的工程應(yīng)用意義。
[Abstract]:In the field of precision measurement technology, machine vision measurement technology is a new technology, which can realize the functions of measuring the dimension of parts, tracking the vision of the target and analyzing the motion of the mechanical device. The machine vision measurement technology uses the computer instead of the "human eye", sees the image and understands the image, extracts the information feature of the digital image collected by the camera, and finally completes the visual detection. Shaft parts are one of the most widely used parts in all kinds of mechanical devices. With the exploration and progress of technology, the precision and running speed of mechanical devices are becoming more and more demanding. In the process of machining, it is often necessary to process the shaft parts with high precision, so it is very important to measure the diameter of shaft parts accurately. Based on the geometrical characteristics of shaft parts and optical imaging process, a method for measuring axis diameter is presented. Firstly, according to the principle of pinhole imaging, the camera imaging model is established and the camera calibration technology is analyzed. Based on the plane calibration method proposed by Zhang Zhengyou, two radial and two tangential distortions are considered in the distortion model to correct the distortion of the optical lens, and the calibration accuracy of the internal and external parameters of the camera is improved. In order to improve the precision of camera parameter calibration, the appropriate number of corner detection points and the illumination intensity of backlight are determined by camera calibration experiments. Then, in order to improve the accuracy of image measurement, it is very important to obtain the sub-pixel edge position from the image. In this paper, several common sub-pixel edge detection methods, including gray moment method, spatial moment method, Gao Si fitting method, hyperbolic tangent fitting method, gradient interpolation method and three-point interpolation method, are theoretically analyzed. Finally, the error of actual measurement dimension is taken as the evaluation criterion of sub-pixel edge detection accuracy. Through the measurement experiment, the gradient interpolation method with good stability and high detection accuracy is determined. As a subpixel edge detection method for axis diameter measurement in this paper. Then, according to the imaging process of the axis parts under the camera, the mathematical model of the axis diameter measurement is established, and the error caused by the axis is not parallel to the image plane is reduced. Then, the unknown parameters in the mathematical model of axis diameter measurement are calibrated. Finally, considering the practicability and convenience of measuring axis diameter, a calibration coefficient method based on the ratio of real axis diameter to pixel diameter is proposed. Finally, the accuracy of the proposed method and the practicability of the calibration coefficient method are verified by the experimental results. The measurement results of the two methods are compared, and the error factors affecting the measurement accuracy are analyzed to determine the direction for improving the measurement accuracy. For the development of machine vision measurement technology, the research work in this paper has certain engineering application significance.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TH133.2;TP391.41

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