基于机器视觉的拆回旧电表参数信息检测技术研究

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。

Research on detection technology of parameter information of old electric meters removal based on machine vision

In view of the problem of low accuracy and low efficiency of manual verification for the recycling classification of old smart meters, this paper proposes a method of parameter information detection based on machine vision. By detecting the rated parameter information of smart meters, the meter is completed Classified recycling work. First, 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 use the histogram equalization to process the extracted image. To enhance the contrast between the background and the target area, obtain a high-quality meter image, improve the Canny edge detection algorithm through the OTSU algorithm, improve the adaptability of the image threshold range, obtain a more complete image appearance contour, and finally the image Perform character segmentation processing to obtain the rated parameter information of the meter. After experimental verification, this method can accurately detect the rated parameter information of the identification plate of the electric meter. The experimental data shows that the detection accuracy rate is 99.5%, and the average test time of each meter is 0.62s, which greatly saves the verification time of the meter classification work Improve the efficiency and accuracy of the work.