Due to the introduction of MonoDepth2， unsupervised monocular ranging has made great progress in the field of visible light. However， visible light is not applicable in some scenes， such as at night and in some low-visibility environments. Infrared thermal imaging can obtain clear target images at night and under low-visibility conditions， so it is necessary to estimate the depth of infrared image. However， due to the different characteristics of visible and infrared images， it is unreasonable to migrate existing monocular depth estimation algorithms directly to infrared images. An infrared monocular ranging algorithm based on multiscale feature fusion after improving the MonoDepth2 algorithm can solve this problem. A new loss function， edge loss function， was designed for the low texture characteristic of infrared image to reduce pixel mismatch during image reprojection. The previous unsupervised monocular ranging simply upsamples the four-scale depth maps to the original image resolution to calculate projection errors， ignoring the correlation between scales and the contribution differences between different scales. A weighted Bi-directional Feature Pyramid Network （BiFPN） was applied to feature fusion of multiscale depth maps so that the blurring of depth map edge was solved. In addition， Residual Network （ResNet） structure was replaced by Cross Stage Partial Network （CSPNet） to reduce network complexity and increase operation speed. The experimental results show that edge loss is more suitable for infrared image ranging， resulting in better depth map quality. After adding BiFPN structure， the edge of depth image is clearer. After replacing ResNet with CSPNet， the inference speed is improved by about 20 percentage points. The proposed algorithm can accurately estimate the depth of the infrared image， solving the problem of depth estimation in night low-light scenes and some low-visibility scenes， and the application of this algorithm can also reduce the cost of assisted driving to a certain extent.