基于光切法的葡萄硬枝嫁接切削面粗糙度图像检测算法

为了实现葡萄硬枝嫁接苗木切削面粗糙度的检测,该文基于光切法测量原理,搭建了切削面粗糙度图像检测系统,研究了特征提取的图像检测算法。为获取较长的取样长度,采用了图像拼接技术,并提出了一种自动制取匹配模板的方法,拼接算法测试结果表明:每多拼接一幅粗糙度特征图像,运行时间平均增加1.104 s,取样长度平均增加1 131.77μm;采用了模糊集合理论对拼接后的粗糙度特征图像进行灰度变换,可以有效保证图像分割后单侧边缘的完整;采用了人机交互的方式对粗糙度特征二值图像像素进行区域操作,可以滤除因切削面自身含有的导管腔、管胞腔而导致的缺陷轮廓,从而提高粗糙度计算的准确度;提出了一种逐列遍历图像提取单侧边缘的方法,通过对单侧边缘进行计算,可以得到粗糙度高度参数Ra与Rz的值。将该粗糙度图像检测算法与基恩士VK-X200形状测量激光显微系统进行了粗糙度检测对比试验,结果表明,该文提出的粗糙度图像检测算法测得Ra的相对误差为6.73%,在测量误差允许范围内,该文基于光切法测量原理的图像检测算法,用于检测葡萄硬枝嫁接苗木切削面粗糙度,具有较高的精度和良好的可行性,为进一步研究切削参数对切削面粗糙度以及对苗木嫁接成活率的影响提供了技术支撑。

Image detection algorithm for cutting surface roughness of grape hard branch grafting based on light-section method

In order to detect cutting surface roughness of grape grafting hard branch, this paper built a detection system of cutting surface roughness and designed an image detection algorithm of feature extraction based on measurement principle of light-section method. The detection system was made up of 9J light-section microscope, industrial camera and computer. Sampling length was an important role in the calculation of roughness. In order to make up the shortage of single sampling length (1420μm), this paper applied image mosaic technology to images acquired by multiple sampling, and thus got a longer sampling length. Meanwhile, a method was proposed to product matching template automatically, which could avoid the manual operation and improve the efficiency of image mosaic when matching the reference image and target image based on gray level information. Then the image mosaic algorithm was tested and the results showed that, the average running time was increased by 1.104 s and the average sampling length was increased by 1131.77μm when the number of mosaic image increased by a roughness feature image. Moreover, fuzzy set theory was applied to the process of gray-scale transformation and could effectively guarantee the integrity of the single edge of the image after segmentation. In this paper, Otsu algorithm was used to segment image. In order to filter the defeat profile due to catheter lumen and tracheid cavity contained by cutting surface itself, local pixels of the roughness feature binary image were operated by way of human-computer interaction. The pixels of the defect position in the foreground image were set to zero and not involved in the subsequent roughness calculation. A method was also proposed to extract the single side edge by scanning pixels of per column one by one, the first pixel whose value was not zero at each column belonged to edge pixels set. According to the measurement principle of light-section method, the corresponding position relation could be established between the single edge extracted from roughness feature image and roughness profile in one place of grape cutting surface. So the value of roughness height parametersRa andRz could be obtained by calculating the single edge. In order to verify the feasibility of the proposed method, the comparative experiment was conducted between the detection system built and KEYENCE VK-200 laser microscope. The experimental results showed that the relative error of Ra measured by proposed method was 6.73%, which was within the allowable range of measurement error, the image detection algorithm based on light-section method has good feasibility when applied to measure cutting surface roughness of grape hard branch. The study provides technical support for the further research of the impact of cutting parameters on cutting surface roughness and grafting survival rate of grape hard branch.