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Image fusion technology can integrate visible and infrared thermal image from different sensors and retain the feature data and complementary information in fused images. Condition monitoring of transmission lines is beneficial to improve the stability for power grid operation. Image sensors that are developed rapidly in transmission line application has advantages such as non-contact measuring, automatic monitoring and unmanned aerial vehicle carrying. With the development of machine learning technology, many deep learning networks are used in visible and infrared thermal image fusion of transmission line. Firstly, the image fusion methods based on convolutional neural network, autoencoder network and generative adversarial network which advantages and applicable conditions of different frameworks are compared. Secondly, the evaluation indicators of fused images are proposed, and the combined evaluation method based on multiple indicators are illustrated. Then, focused with the demand of transmission line operation condition monitoring, different deep learning algorithm frameworks have been verification by visible and infrared thermal images gathered from the transmission line. Finally, these algorithms are compared with evaluation indicators and the research direction of the future developments are proposed.
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