Depth estimation of monocular infrared images based on attention mechanism and graph convolutional neural network

The depth estimation of objects in the scene is a key issue in the field of the unmanned driving. The infrared images are helpful to solve the depth estimation problem under poor light conditions. Aiming at characteristics of unclear infrared images texture and insufficient edge information, a combination of attention mechanism and graph convolutional neural network was proposed to solve the problem of monocular infrared images depth estimation. First of all, in the depth estimation problem, the depth information of each pixel in the image was not only related to the depth information of its surrounding pixels, but also needed to consider the depth information of a larger range of other pixels. The attention mechanism could be effectively extract the pixel-level global depth information association of images. Secondly, the features obtained based on the depth information association could be considered as non-Euclidean data, and the graph convolutional neural network (GCN) was further used for reasoning. Finally, in the training phase, the continuous depth estimation regression problem was transformed into the classification problem, which made the training process more stable and reduced the learning difficulty of the network. The experimental results show that the proposed method has obtained good results on the infrared data set NUST-SR. When the threshold index is less than 1.25³, the accuracy rate is improved by 1.2%, which is more advantageous than other methods.