Infrared and Visible Image Registration for Power Equipments Based on Deep Learning

A registration fusion method of infrared and visible images of power equipment based on deep learning is proposed that aims at problems with difficult and long registration time of infrared and visible images of existing power equipment. In this study, feature extraction and feature matching are combined in a deep learning framework to directly learn the mapping relationship between image block pairs and matching labels for subsequent registration. In addition, a self-learning method using infrared image and its transform image to learn the mapping function is proposed to alleviate the problem of insufficient infrared image samples during training Simultaneously, transfer learning is used to reduce the training time and accelerate the network framework. The experimental results show that the performance index of this method is significantly improved compared with the other four registration algorithms. The average accuracy of this method is 89.909, which is 2.31%, 3.36%, 2.67%, and 0.82% higher than that of the other four algorithms, respectively. The average RMSE of this method is 2.521. Compared with the other four registration algorithms, the algorithm is reduced by 14.68%, 15.24%, 4.90%, and 1.04%, respectively. The average time of the algorithm is 5.625 s, which is reduced by 5.57%, 6.82%, 2.45%, and 1.75% respectively. The efficiency of infrared and visible image registration of the power equipment must be effectively improved.