摘要
针对远心镜头因自身特性无法调节视场大小,难以适应直线轴承测量时一次性获取零件完整图像的问题,利用HALCON图像处理软件通过图像拼接方法进行直线轴承的长度测量。综合考虑特征点提取和边缘轮廓获取效果,采用背光与正向照明相结合的方案,建立了基于组合照明的直线轴承测量系统;在图像拼接中,提出了一种在图像近似重合区域内使用图像金字塔分层搜索的方法进行特征点检测与匹配,以提高图像拼接效率;最后通过Canny算子和最小二乘法拟合进行图像边缘定位,完成长度测量。实验结果表明:组合光源能更好地兼顾图像表面特征点与边缘轮廓提取效果;提出的拼接方法检测时间在0.2 s左右,相比于传统方法检测时间减少了88%;基于图像拼接的视觉测量误差小于0.1 mm,测量重复性上相比于传统方法更稳定,测量最大标准差为0.005。因此,保证了视觉系统的检测效率、精度以及稳定性,在工业自动化检测方面具有一定的理论依据和实用价值。
Aiming at the problem that the telecentric lens cannot adjust the field of view due to its own characteristics, and it is difficult to adapt to the problem of obtaining the complete image of the part at one time when measuring the linear bearing, the HALCON image processing software is used to measure the length of the linear bearing through the image stitching method. Considering the effect of feature point extraction and edge contour acquisition, using the combination of backlight and forward lighting, a linear bearing measurement system based on combined lighting is established;in image stitching, a method of using images in the approximate overlap area of the image is proposed. The method of pyramid hierarchical search performs feature point detection and matching to improve the efficiency of image stitching;finally, the edge location of the image is performed by the Canny operator and the least square fitting method, and the length measurement is completed. The experimental results show that: the combined light source can better take into account the effect of image surface feature points and edge contour extraction;the detection time of the proposed stitching method is about 0.2 s, which is 88% less than the traditional method;The visual measurement error based on image stitching is less than 0.1 mm. In terms of measurement repeatability, it is more stable than traditional methods, and the maximum standard deviation of measurement is 0.005. Therefore, the detection efficiency, accuracy and stability of the vision system are guaranteed, and it has a certain theoretical basis and practical value in industrial automation detection.
作者
李纪强
孔令华
花海燕
Li Jiqiang;Kong Linghua;Hua Haiyan(School of Mechanical and Automotive Engineering,Fujian University of Technology,Fuzhou 350118,China;Digital Fujian Industrial Manufacturing IoT Lab,Fuzhou 350118,China)
出处
《电子测量技术》
北大核心
2022年第8期148-154,共7页
Electronic Measurement Technology
基金
国家自然科学基金(51905100)
福建省自然科学基金(2019J05111)项目资助。