Unsupervised deep learning image stitching model assisted with infrared images

The rapid development of artificial intelligence facilitates the improvement of image processing algorithms. For an intelligent inspection robot, the ability to analyze the environment through image collection plays an important role. It needs to collect multiple images of the same scene from different angles of view so as to make a thorough analysis about the environment it locates and generate further decisions. Therefore, a technique called image stitching is used. Currently, the development of image stitching algorithms is getting mature – multiple algorithms have already been proposed based feature extraction techniques. However, these existing algorithms are usually unable to handle the problem of parallax existing in real world image. Therefore, in order to solve it, we proposed an unsupervised deep learning image stitching algorithm, which uses infrared images to provide auxiliary information. We utilized our own equipment to collect real world images in visible light and infrared. Finally, we implemented our own model and other popular existing image stitching algorithms and compared and contrasted their performance on our dataset. The results showed that our model has the best performance in all aspects than other algorithms on the dataset, indicating the strong advantages of deep learning methods on image stitching tasks