【作者】 王彩云；

【导师】 王巧华；

【作者基本信息】 华中农业大学 ， 农业电气化与自动化， 2016， 硕士

【摘要】 鸭蛋表面脏污程度比鸡蛋要严重的多,且表面脏污越严重,蛋壳表面所携带的微生物也越多,这样鸭蛋之间的交叉感染就变得更容易。现阶段我国禽蛋表面脏污检测还停留在禽蛋表面的脏污检测,根据禽蛋表面脏污进行分级、定级的研究较少。对于鸭蛋的新鲜度检测,现阶段主要采用人工照蛋检测和打破检测。但前者主要由人工靠经验检测,由于人的精力和反应能力都受人体自身的限制,无法长时间、高效率的工作,以致提高生产成本,且对检测结果也有很大的影响;后者又具有很大的破坏性,在实际生产中毫无优势。因此,研究产地鸭蛋表面脏污和新鲜度的快速无损检测方法,不仅具有重要的科学意义,还具有实际应用前景。本研究以鸭蛋为研究对象,利用机器视觉系统对鸭蛋的图像进行采集,通过对图像进行预处理和特征参数的提取,得到鸭蛋表面脏污面积与整蛋面积比(简称脏污面积比)和脏污块数,以及白壳鸭蛋透射图像的R、G、I分量灰度均值,蛋心面积与整蛋面积比(简称蛋心面积比)和气室面积与整蛋面积比(简称气室面积比)等内外部品质指标,并建立相关检测分级模型,实现对鸭蛋表面脏污和新鲜度的检测分级。主要研究内容和研究结果如下:(1)搭建了鸭蛋表面脏污和新鲜度的图像采集系统。利用多个单筒照蛋灯对表面脏污鸭蛋进行透射照明,每枚鸭蛋从进入暗箱到移出暗箱被拍照三次,以保证图像采集的完整性;利用单个照蛋灯对经初洗后的白壳鸭蛋进行透射照明,采用高清相机拍照获取图像,便于后续的图像特征参数提取。(2)确定了鸭蛋图像的预处理方法。通过将彩色图像B分量二值化得到图像中的漏光部分,并重组成三维图像后与彩色图像相减,实现去除鸭蛋表面脏污图像中因装置和鸭蛋之间的间隙而产生的漏光;利用图像R分量设定阈值二值化,并重组后与彩色图像相乘,实现去除鸭蛋新鲜度图像中的漏光。(3)提取了鸭蛋表面脏污及新鲜度图像的特征参数。选取鸭蛋表面脏污面积比和脏污块数,作为判别鸭蛋表面脏污程度的特征参数,并通过多次实验确定判别鸭蛋表面脏污程度的特征参数范围;采用hough变换的直线检测方法来提取气室面积,利用对彩色图像的G分量进行相关处理来获得蛋心区域面积,选取鸭蛋蛋心面积比、气室面积比和图像R、G、I分量灰度均值,作为判别鸭蛋新鲜与否的特征参数。(4)建立了鸭蛋表面脏污程度及新鲜度分级模型。确定表征鸭蛋表面脏污程度的参数范围,以及综合鸭蛋三次拍照图像的判别方法,最后利用MATLAB软件建立鸭蛋表面脏污程度分级模型,综合判别正确率达95.00%以上;选择最小二乘支持向量机建立鸭蛋新鲜度分级模型,按2:1的比例将所有的实验组样本分为训练集和预测集,以鸭蛋图像的R、G、I分量灰度均值、蛋心面积比和气室面积比这5个指标作为特征参数,训练集和预测集的分级正确率分别达96.92%、93.85%。更多还原

【Abstract】 The surface fouling degree of duck egg is much more serious than that of chicken eggs. More over, the more serious the smudginess is, the more microorganism there are. Then, the cross infection gets easier. At present, the detection of egg surface smudges still rests on the smudge detection, but not its classification according to the fouling degree. As to the freshness detection of duck egg, most of the egg quality detection still depends on experience detection manually. Because of the restriction of human vigor and reaction capacity, this not only may increase cost, but also have a great influence on detection result. Therefore, doing a research on the rapidly egg quality detection is of great scientific significance and realistic application prospect.In this research, duck eggs are the researching object, and machine vision technology is used to collect duck egg images, preprocess and extract characteristic parameters. Then, the fouling area ratio which is short for the ratio of duck egg’s surface fouling area to the whole area has been gotten, as well as the number of smudges. Also, in the duck egg’s transmission images with white shell, the yolk area ratio has been obtained as well as air chamber area ratio. Lastly, the related detection and classification model has been established which makes the duck egg’s surface smudges and freshness detection and classification come true. The following contents are the researching results.(1) The image acquisition system of duck egg’s surface smudges and freshness has been established. Several monocular egg candling lights were used to illuminate dirty duck eggs by the method of transmission. And every duck egg was photographed three times in order to ensure the integrality of collected images. As to rough cleaning duck eggs with white shells, single egg candling light was applied to illuminating eggs by the method of transmission. And the images of detecting objects were collected by employing high-definition camera convenient for the extracting of characteristic parameters.(2) The preprocessing method of duck egg images has been determined. The light-leaking section was gained through the B component binarization. Then, the binary image was recombined into three-dimensional image. And the recombined image was subtracted from the original color image which realized the remove of light leak because of the gap between equipment and duck eggs. As to the light-leaking in duck egg’s freshness images, threshold value was set when the R component binarized. Also, it was recombined and multiplied with original color image so as to eliminate the light leak.(3) The characteristic parameters of duck egg’s surface smudges and freshness have been extracted. The fouling area ratio and the number of smudges were chosen as characteristic parameters to distinguish duck egg’s fouling degree. And the range of characteristic parameters were determined by several experiments. A straight line detection method of hough transformation was used to extract air chamber area. Duck egg’s yolk area was obtained by doing related processing on G component. The yolk area ratio, air chamber area ratio and the gray average of R, G, I components were chosen as characteristic parameters to distinguish whether a duck egg was fresh or not.(4) The classification model of duck egg’s surface fouling degree and freshness have been established. The range of characteristic parameters to distinguish duck egg’s surface fouling degree was determined. Synthesizing the distinguishing method of duck egg’s three photographed images, the classification model of fouling degree was established by using MATLAB software and the accuracy was above 95%. The least square support vector machine was used to establish classification model of duck egg’s freshness. According to the proportion of 2 to 1, all of the samples were divided into training set and prediction set. The above 5 indexes were used as the characteristic parameters. The classification model established, the accuracy of training set and prediction set were 96.92% and 93.85% respectively.更多还原