Automated Type Identification and Size Measurement for Low-Voltage Metering Box Based on RGB-Depth Image

Abstract

The low-voltage metering box is a critical piece of equipment in the power supply system. The automated inspection of metering boxes is important in their production, transportation, installation, operation and maintenance. In this work, an automated type identification and size measurement method for low-voltage metering boxes based on RGB-D images is proposed. The critical components, including the door shell and window, connection terminal block, and metering compartment in the cabinet, are segmented first using the Mask-RCNN network. Then the proposed Sub-Region Closer-Neighbor algorithm is used to estimate the number of connection terminal blocks. Combined with the number of metering compartments, the type of metering box is classified. To refine the borders of the metering box components, an edge correction algorithm based on the Depth Difference (Dep-D) Constraint is presented. Finally, the automated size measurement is implemented based on the proposed Equal-Region Averaging algorithm. The experimental results show that the accuracies of the automated type identification and size measurement of the low-voltage metering box reach more than 92%.