双目视觉和机器学习的拆回电能表微颗粒清洗系统

Microparticle cleaning system of detachable electric energy meter based on binocular vision and machine learning

传统拆回电能表清洗采用人工、毛刷或离子吹风方式,导致污渍识别准确率不高,清洗效果不佳,因此,采用双目视觉和机器学习的方法对其进行改进。该系统以控制中心为核心,通过通信协议连接识别模块和清洗服务子系统,构建系统框架。建立双目视觉系统,对双目摄像机进行神经网络标定后采集拆回电能表图像信息,通过坐标转换和视差计算完成污渍定位。将定位结果输入卷积神经网络,通过网格划分、特征映射等操作完成拆回电能表污渍识别。在清洗服务子系统中设计清洗装置,依据污渍识别结果针对性清洗拆回电能表,完成清洗系统设计。以清洗500只电能表为例进行测试,结果表明:文中方法耗时22 min,识别误差约为10%,而且清洗后的电能表的检定效果由原来的70%提高到97%。

Manual,brush or ion blowing methods are adopted in the clearing system of detachable electric energy meter,resulting in low accuracy of stain identification and poor cleaning effect.Therefore,the methods of binocular vision and machine learning were used to improve it.The system takes the control center as the core,the identification module was connected with the cleaning service subsystem through the communication protocol,and the system framework was constructed.A binocular vision system was established.After the binocular camera was calibrated by neural network,the image information of disassembled electric energy was collected,and the stain location was completed through coordinate conversion and parallax calculation.The positioning results were input into the convolution neural network(CNN),and the dirt identification of the disassembled watt hour meter was completed through grid division,feature mapping and other operations.The cleaning device was designed in the cleaning service subsystem,and the electric energy meter was removed according to the stain identification results to complete the design of the cleaning system.Taking cleaning 500 electric energy meters as an example,the experimental results show that the method in this paper takes 22 minutes,the recognition error is about 10%,and the verification effect of the cleaned electric energy meter is improved from 70%to 97%.