Research on an Intelligent Verification Method for Hidden Dangers in Power Metering Boxes Based on Deep Instance Segmentation

Aiming at the problems of uneven image quality, complex hidden danger types, and low efficiency of manual verification in the operation and maintenance of power metering boxes, this paper proposes an intelligent verification framework based on deep instance segmentation. The method uses an improved CondInst dynamic convolution network to achieve precise segmentation of the metering box components, and combines a multi-scale quality evaluation module with a hidden danger classification system to realize automatic analysis of the 4 million provincial-level metering box images. Experiments show that the intelligent verification framework in this paper achieves an identification accuracy of 92.7% for key defects such as box damage and missing seals. By establishing a three-level analysis architecture of "quality features-appearance features-site features", it provides intelligent technical support for scenarios such as the digital archiving of metering boxes and equipment renovation.