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In the development of modern intelligent Ubiquitous Electric Internet of Things (UE-IoT), infrared thermal imaging always plays an important role in automated early-warning detection of developing failures of critical assets such as transformers, disconnects and capacity banks in electrical power substation non-intrusively. However, the low resolution and contrast of infrared images hinder the subsequent analysis and recognition of fault points. In contrast, visible images present abundant texture details of the equipment without thermal information. In order to assist the detection of fault points, this paper proposes a Generative adversarial networks (GAN) based infrared and visible image fusion method to produce a composite image with enhanced edges and better quality. The edge loss function is added to represent the perceptual edges. In the discriminator, the proposed method improves the texture similarity between fusion image and visible image by minimizing the Wasserstein distance in VGG (Visual Geometry Group network) feature space. The experimental results show that the fault regions become more salient and the details are enhanced. In this way, it can facilitate the detection of fault points both reliably and accurately.

Fusion of Infrared-Visible Images in UE-IoT for Fault Point Detection Based on GAN