Domain self-adaptive equipment operation inspection system and method

Abstract

The invention discloses a domain self-adaptive equipment operation inspection system and method. An image acquisition processing module is used for acquiring a to-be-detected inspection image of a power transmission line and preprocessing the image; the to-be-detected inspection image is input into a preset weather condition classification model to identify weather conditions to which the to-be-detected inspection image belongs, and a weather condition classification result is obtained; a corresponding preset domain self-adaptive equipment operation inspection model is selected according to the weather condition classification result; and the to-be-detected inspection image is input into a preset domain self-adaptive equipment operation inspection model to obtain a detection result of theto-be-detected inspection image, wherein the detection result comprises one or a combination of equipment category, working condition and position information of a detection object in the to-be-detected inspection image. According to the invention, the to-be-detected sample of the high-voltage transmission line domain self-adaptive equipment operation detection system is not restricted by sample annotation and regional or weather conditions, and the equipment operation detection result of the target domain in the domain self-adaptive scene has the same detection performance as that of the source domain.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence, information processing technology, and grid operation inspection and maintenance, and in particular to a domain adaptive device operation inspection system and method. Background technique [0002] In recent years, artificial intelligence has played an increasingly important role in the field of power grid inspection and maintenance technology. The high-voltage transmission line equipment inspection system based on deep learning conducts supervised training on massively labeled inspection image data, which becomes a necessary means for power grid inspection, improves detection accuracy, and shortens fault handling time. [0003] In a specific high-voltage transmission line equipment inspection system for power grid operation inspection and maintenance, the inspection image to be detected is collected by the terminal equipment, input to the convolutional neural network for feature...

AI Technical Summary

Problems solved by technology

Unfortunately, it is expensive and infeasible to collect enough annotated training samples from different geographical scenes or different weather

At the same time, different regional scene backgrounds and under rainy or foggy conditions, the edge and color features brought about by light, rain streaks, fog, and blur greatly cause significant differences between the feature distributions of inspection images under different weather conditions. Domain differences, such domain differences or domain shifts will cause poor performance of the model in the new domain, that is, the inspection model trained on sunny weather samples is difficult to generalize to cross-regional or other weather condition inspection tasks

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