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1、 机器视觉论文：基于机器视觉的排种器性能检测技术研究【中文摘要】准确地检测排种器的技术参数是检验排种器工作性能以及研制精密排种器的技术关键。本文针对现有黄油胶带法试验台存在的不足,采用输送带上铺砂原理设计研制了铺砂型排种器试验台,利用砂层的缓冲作用防止种子着落时弹跳。并应用机器视觉技术对排种图像进行了采集与处理,实现了自动的种子识别与粒距测量,在此基础上实现了排种器性能参数的实时自动分析。本文主要开展了以下研究工作并得出相关结论：(1)运用铺砂原理研制了排种器试验台,相对于传统的涂油方式,具有种子可回收利用、无污染、具有良好机器视觉参照背景等优点。(2)应用工业相机和光源构建了摄影系统,通过设
2、置专用摄影室保证了稳定的摄影效果。并对图像进行了标定,对摄影参数进行了优化。(3)建立了基于RS485总线的控制系统,实现了对各运动部件的精密控制。(4)创新地提出了基于感应参考标记与图像参考标记相结合的序列图像无缝拼接技术。其中感应参考标记保证了在不同的输送带运行速度条件下,图像序列的稳定性,通过图像参考标记实现相邻图像的无缝拼接。(5)针对图像特点,制定了相应的图像处理流程。主要包括：图像区域切割、图像分割、连通区域标记、图像特征量提取、图像无缝拼接。(6)分析并优化了图像处理算法：如针对不同识别区域图像特点应用了不同的阈值计算方法；运用基于连通区域标记的目标识别算法实现了参考标记与种子对
3、象的识别;提出了基于保存序列种子位置数据的虚拟图像拼接方法。(7)对图像采集与拼接效果进行了定性试验分析,结果表明,拼接图像与实际图像具有良好的相关性,证明了图像处理算法的可行性。(8)对图像中种子识别与定位以及粒距的测量进行定性定量分析,结果表明,图像处理结果与种子实际分布相符。(9)应用本系统对排种器进行了排种性能测试试验,并与手工测量数进行了对比,对比结果表明,系统工作性能符合检测要求。【英文摘要】Detecting accurately the technical parameters of seed metering device is the key to test the dev
4、ice performance and develop precise seed-metering device.This paper analyses the insufficiencies of butter tape test bench, suggesting developing seed-metering test bench with sand-paving conveyor belt and preventing the seeds from bouncing through the buffer action of sand layer. Machine vision tec
5、hnology is also applied to collect and process the seeding image to realize the automatic recognition of seed and seed spacing measurement, which, on this basis, achieves the real-time automatic analysis of the device performance parameters.This paper mainly discusses the following researches and dr
6、aws the relevant conclusions:(1) Seed-metering test bench is developed by applying the principles of paving sand. Compared with traditional way of oiling, this bench owns advantages of seed recyclability, non-pollution, and background with good machine vision and so on.(2) Photography system is cons
7、tructed by applying the industrial cameras and light. A stable photograph effect can be guaranteed by setting special photograph studio. The image is calibrated and camera parameter is optimized.(3) The control system based on RS 485 Bus is established, which realizes the precise control of all movi
8、ng parts.(4) Sequence seamless image mosaic technology is put forward innovatively based on the combination of induction reference mark and image reference mark, in which induction reference mark ensures the stability of image sequence under different speeds of conveyor belt and image reference mark
9、 realizes the seamless mosaic of adjacent images.(5) For the image features, corresponding image processing procedure is formulated which mainly includes image region segmentation, image segmentation, connected region labeling, image feature amount extraction and image seamless mosaic.(6) Image proc
10、essing algorithms is analyzed and optimized, for example, different threshold calculation methods are used for different image recognition features; identification of reference mark and seed is realized based on the target recognition algorithm of connected region labeling; virtual image mosaic meth
11、od is proposed by saving image sequence location data.(7) Qualitative test analysis is conducted for the image acquisition and mosaic effect which turns out that mosaic image and the actual image has a good correlation and proves the feasibility of image processing algorithms.(8) Qualitative and qua
12、ntitative analysis of seed identification and location and measurement of seed spacing are conducted for the image which shows that the results of image processing are in line with the actual distribution of seeds.(9) The system is applied to the seed-metering device to test the performance of the m
13、etering accuracy which, compared with the number of manual measurements, turns out that system performance meets the requirement of detection.【关键词】机器视觉 排种器 铺砂 性能检测【英文关键词】machine vision seed-metering device pave sand performance detection【目录】基于机器视觉的排种器性能检测技术研究摘要4-5Abstract5-6第一章 概述10-161.1 排种器性能检测概论1
14、01.2 国内外研究现状10-141.2.1 排种器性能检测主要方法及研究现状10-131.2.2 基于机器视觉的排种器性能检测现状分析13-141.3 研究目的及意义141.4 研究内容与方案14-161.4.1 研究的主要内容14-151.4.2 研究方案15-16第二章 检测系统建立16-352.1 系统总体结构及工作原理16-172.2 输送带铺砂系统17-212.2.1 输送系统17-182.2.2 排砂系统18-202.2.3 排种系统20-212.2.4 砂种分离系统212.3 摄影系统21-262.3.1 工业相机选型21-232.3.2 相机安装与标定23-242.3.3 照
15、明系统24-252.3.4 相机操作方法25-262.4 图像序列无缝拼接方法26-282.5 系统控制原理28-342.5.1 系统驱动方案28-292.5.2 控制系统原理29-302.5.3 总线通迅协议30-342.6 本章小结34-35第三章 图像处理算法分析35-503.1 图像区域切割35-363.2 图像分割算法36-413.2.1 算法选择36-383.2.2 标记识别区域图像特性分析383.2.3 基于迭代法的标记识别区域图像分割算法研究38-393.2.4 砂带背景种子区域图像特性分析39-413.3 基于连通区域标记的目标识别算法研究41-443.3.1 二值图像的连通
16、区域及其标记算法41-423.3.2 连通区域标记运算过程42-433.3.3 等价标记处理算法43-443.4 图像特征量的提取44-453.4.1 连通区域形心的确定443.4.2 连通区域面积信息的提取44-453.4.3 图像参考标记判断453.4.4 种子合法性判断453.5 图像序列无逢拼接算法45-483.5.1 拼接原理分析45-463.5.2 图像序列中种子位置的计算46-473.5.3 拼接边界上种子的处理47-483.6 本章小结48-50第四章 检测试验与应用效果分析50-584.1 图像采集与拼接效果定性分析50-534.2 种子识别与粒距测量应用效果分析53-564.3 排种器性能检测试验56-574.4 本章小结57-58第五章 结论与建议58-595.1 研究结论585.2 建议与讨论58-59参考文献59-61致谢61-62作者简介62