Infrared image power equipment segmentation method based on generative adversarial network

Abstract

The invention discloses an infrared image power equipment segmentation method based on a generative adversarial network. The method comprises the following steps: S1, preparing network input data; s2,constructing a generative adversarial model: Cycle-Gan; s3, designing a thermal imaging image semantic segmentation model; and S4, designing a loss function of the segmentation network. In the present invention, the problem of how to eliminate the difference between different modal data of a thermal imaging image is solved, an existing pre-training segmentation model is fully utilized, a generative adversarial network is utilized to establish a relationship between different modal data, so that a thermal imaging image can fully utilize the advantages of an existing semantic segmentation modelbased on a natural image, and a thermal imaging image semantic segmentation model with more excellent performance is realized.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an infrared image power equipment segmentation method based on a generative confrontation network. Background technique [0002] For power systems, technologies such as intelligent inspection robots and drones are gradually replacing inefficient manual inspections. In order to more comprehensively monitor the abnormality of equipment in the power system, in addition to collecting natural images under normal lighting conditions, thermal imaging images collected through infrared detection and diagnosis technology are also used in practical applications when lighting conditions are not ideal. The collected thermal imaging images contain information about whether the equipment is abnormal. It is particularly important to use artificial intelligence technology to realize automatic abnormality detection of thermal imaging images. To realize the intelligent analysis of thermal...

AI Technical Summary

Problems solved by technology

[0005] However, due to the structural differences between natural color images and thermal imaging images, it is a challenging task to directly process them with widely adopted technical schemes in natural images.

The two main technical difficulties are: the distribution of thermal imaging images and natural images is very different, and there is currently no system for processing thermal imaging images; the deep learning model widely used at present requires reasonable pre-training is necessary, but there is no such parametric model for thermal imaging images

[0006] The existing thermal imaging semantic segmentation system directly builds a depth model and uses the labeled images of thermal imaging to learn it, which does not make good use of the advantages of the depth model currently researched on natural images.

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