Electric power image data augmentation method based on generative adversarial network

Abstract

The invention discloses an electric power image data augmentation method based on a generative adversarial network. The method comprises the following steps: obtaining an original image during electric power system inspection; performing image processing on the original image, eliminating interference and influence of noise and the like in the original image, and obtaining a high-quality target image; performing feature enhancement on the target image, and extracting image features; and performing game synthesis of the target image and the background image through a generative adversarial network method to generate new image data. According to the scheme, data augmentation is carried out on the power image through the generative adversarial network method, image annotation does not need tobe carried out on the synthesized image, manpower and material resource cost is saved, and meanwhile the synthesis method is scientific, reasonable, high in augmentation result quality, high in efficiency and high in scheme feasibility.

Description

technical field [0001] The invention relates to the technical field of image processing, and specifically designs a power image data augmentation method based on a generative confrontation network. Background technique [0002] As an important infrastructure for people's livelihood and national defense construction, the power grid plays a decisive role in the transmission and distribution of electric energy. In recent years, with the continuous expansion of the scale of my country's power grid, the area covered by transmission lines and substations is also increasing. Therefore, it is particularly important to ensure the safe operation of the power grid. Helicopters, unmanned aerial vehicles and intelligent robots are gradually popularized in power inspection due to their high outdoor operation efficiency, low operation risk and flexible operation, bringing a large amount of inspection images and video data to the power system. Compared with the traditional inspection metho...

AI Technical Summary

Problems solved by technology

However, since the promotion and application of helicopters, drones and intelligent robots is not long, the amount of image data collected for some fault types (such as damaged bird-proof facilities, dirty insulators, etc.) is small, which cannot meet the needs of deep learning.

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