摘要
针对拆回旧智能电能表的回收分类存在人工检定准确率不高、效率低下的问题,提出了一种基于机器视觉的参数信息检测方法,通过检测智能电能表的额定参数信息,完成电能表的分类回收工作。在以C#与Halcon为软件平台建立智能电能表图像检测系统的基础上,采用Blob分析算法,对图像进行ROI(感兴趣区域)提取,采用直方图均衡化对提取后的图像进行处理,以增强背景与目标区域之间的对比度,获取质量较高的电能表图片,通过OTSU算法对Canny边缘检测算法进行改进,提高图像阈值范围的自适应性,获取更完整的图像外观轮廓,最后对图像进行字符分割处理,得到电能表的额定参数信息。经过实验验证,该方法能够准确检测识别电能表铭牌的额定参数信息,实验数据显示检测准确率达99.5%,平均每台电能表检测耗时0.62 s,很大程度上节省了电能表分类工作的检定时间,提升了工作的效率与准确性。
In view of the problem of low accuracy and low efficiency of manual verification for the recycling classification of old smart meters removal,this paper proposes a method of parameter information detection based on machine vision.By detecting the rated parameter information of smart meters,the classified recycling work of electricity meter is completed.Firstly,based on the establishment of a smart meter image detection system using c#and Halcon as software platforms,the Blob analysis algorithm is used to first extract the ROI(region of interest)of the image,and the histogram equalization is used to process the extracted image to enhance the contrast between the background and the target area,and obtain a high-quality meter image.The Canny edge detection algorithm is improved through the OTSU algorithm,so as to improve the adaptability of the image threshold range and obtain a more complete image appearance contour.Finally,the character segmentation processing is performed for image to obtain the rated parameter information of electricity meters.After experimental verification,this method can accurately detect the rated parameter information of the identification plate of the electricity meter.The experimental data shows that the detection accuracy rate is 99.5%,and the average test time of each meter is 0.62 s,which greatly saves the verification time of the meter classification work,and improves the efficiency and accuracy of the work.
作者
王敏
郑鹏
Wang Min;Zheng Peng(School of Mechanical and Power Engineering,Zhengzhou University,Zhengzhou 450001,China)
出处
《电测与仪表》
北大核心
2023年第9期171-176,共6页
Electrical Measurement & Instrumentation
基金
国家自然科学基金资助项目(51775515)
郑州大学教育教学改革研究与实践项目(2019ZZUJGLX021)。
关键词
机器视觉
智能电能表
图像处理技术
参数信息检测
machine vision
smart meters
image processing technology
parameter information detection
