Abstract:

At present， the unsupervised monocular infrared image depth estimation method is difficult to deal with low texture and low contrast areas， resulting in poor estimation effect， so an unsupervised monocular infrared image depth estimation algorithm based on local plane guide layer is proposed in this paper. The algorithm consists of continuous video frame input， multi-scale feature extraction， ASPP and local planar guidance layer， computational loss， joint training， and output image module. Firstly， by using multiple small-resolution grayscale blocks and multi-scale feature fusion， the problems of blurring edges and occluding objects in infrared images are solved. Secondly， by using the local plane guidance layer to introduce a plane constraint on the depth image， the noise and discontinuity in the depth image are reduced， and the problem of lack of clear processing of low texture areas of the traditional algorithm is solved. The experimental results show that the proposed depth estimation algorithm effectively improves the accuracy of monocular depth estimation and reduces the error， and the Abs Rel， Sq Rel， RMS， RMS（log） on the Iray dataset is 0.262， 3.621， 9.473 and 0.332， respectively， and the accuracy reaches 60.5%， 85.2% and 94.5% when the threshold indicators are less than 1.25， 1.25² and 1.25³.

Key words: unsupervised, infrared image, monocular depth estimation, local planar guidance layer, loss function