A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the re...A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.展开更多
为解决电池模组极柱焊接缺陷检测精度低、效率低的问题,提出了一种基于机器视觉的焊接缺陷检测算法。首先,对采集图像进行预处理操作;其次,通过组件筛选结合改进的Canny算法获取目标区域的无干扰边缘轮廓,为了改善拟合干扰现象,利用基...为解决电池模组极柱焊接缺陷检测精度低、效率低的问题,提出了一种基于机器视觉的焊接缺陷检测算法。首先,对采集图像进行预处理操作;其次,通过组件筛选结合改进的Canny算法获取目标区域的无干扰边缘轮廓,为了改善拟合干扰现象,利用基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对焊接区域的内外圆边缘点集进行分离;然后,采用改进的最小二乘法对内外圆点集分别进行拟合得到精准的焊接区域;最后,以焊接区域内外圆的面积差和同心度来检测焊接面积缺陷和焊偏,通过双向扫面检测法进行焊接区域灰度值遍历,根据对应的灰度值范围和区域大小来检测焊穿和炸点缺陷。实验表明,该算法能够精确拟合焊接区域并准确识别出焊接缺陷,具有较高的检测精度和效率,能够满足工业生产需求。展开更多
基金Supported by Henan Institute of Science and Technology (055031)
文摘A detecting method based on machine vision was put forward to test the performance of seedmeter with corn and soybean seeds as test samples,in which MATLAB software was applied to process image data and analyze the results.The experimental results showed that the mean value of absolute error of the sowing speed for soybean was 0.004-0.68 seed ？ s-1;the mean value of relative error was from 6.5% to 130%,and there were no significant differences of mean value,standard deviation and coefficient of variation of flowing seeds between manual statistics and MATLAB statistics.The machine vision method was proved to be time-saving,labor-saving and no-touching in the seedmeter precision detecting.
文摘为解决电池模组极柱焊接缺陷检测精度低、效率低的问题,提出了一种基于机器视觉的焊接缺陷检测算法。首先,对采集图像进行预处理操作;其次,通过组件筛选结合改进的Canny算法获取目标区域的无干扰边缘轮廓,为了改善拟合干扰现象,利用基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法对焊接区域的内外圆边缘点集进行分离;然后,采用改进的最小二乘法对内外圆点集分别进行拟合得到精准的焊接区域;最后,以焊接区域内外圆的面积差和同心度来检测焊接面积缺陷和焊偏,通过双向扫面检测法进行焊接区域灰度值遍历,根据对应的灰度值范围和区域大小来检测焊穿和炸点缺陷。实验表明,该算法能够精确拟合焊接区域并准确识别出焊接缺陷,具有较高的检测精度和效率,能够满足工业生产需求。
