摘要
针对机械表读时需要耗费大量人力物力的现象,提出了一种基于机器视觉的自动化机械表精度测量方法.首先设计了一个用于图片采集的硬件系统,然后在采集到的图片上,利用改进后的mask-rcnn网络,对指针和各区域进行了提取,其次利用canny算子对表盘进行边缘提取,确定各连通域的质心,根据各质心到12点位与6点位之间连线中点的距离关系,筛选出刻度质心,用刻度质心拟合表盘内圆,确定表盘中心的位置.然后将中心与12点位中心的连线作为测量的基准线,再利用RANSAC最小二乘距离法对各指针的中心线进行提取.通过计算基准线与中心线之间的角度关系,进而达到用机器读取时间的目的,同时针对机械表的读数识别,提出了误差校正原则,大大提高了机械表精度测量的效率.
Nowadays a large amount of manpower and material resources are required for mechanical reading,and thus an automatic mechanical watch accuracy measurement method based on machine vision is proposed in this paper.Firstly,a hardware system for image acquisition is designed,and then the improved mask-rcnn network is used to extract the mechanical pointer area and other area on the captured image.Secondly,the canny operator is used to extract the edge of the dial,and the centroid of each connected domain is determined.According to the distance relationship between the centroids and the midpoint of the line between the 12 o′clock and the 6 o'clock,the scale centroid is selected and used to fit the inner circle of the dial to determine the position of the center of the dial.Subsequently,the connection between the center and the center of 12 o′clock is used as the reference line for measurement,and the center line of each pointer is then extracted by the RANSAC least squares method.By calculating the angular relationship between the reference and center line,the purpose of using the machine to read the time is achieved.At the same time,as for the reading of the mechanical watch,the error correction principle is proposed,which greatly improves the efficiency of the mechanical watch accuracy measurement.
作者
曹思佳
代扬
余洪山
李斐
孙炜
CAO Sijia;DAI Yang;YU Hongshan;LI Fei;SUN Wei(College of Electrical and Information Engineering,National Engineering Laboratory of Robot Visual Perception and Control Technology,Hunan University,Changsha 410082,China;Shenzhen Research Institute of Hunan University,Shenzhen 518057,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第12期86-94,共9页
Journal of Hunan University:Natural Sciences
基金
国家自然科学基金资助项目(61973106,U1913202)
湖南省科技计划重点研发项目(2018GK2021)
航空科学基金项目(201705W1001)
郴州市科技计划项目。
