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

Abstract

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%.

Keywords

• binocular visual system
• convolutional neural network
• electric energy meter
• identification
• positioning
• microparticle cleaning system