Monocular depth estimation method based on deep learning

Abstract

The invention provides a monocular depth estimation method based on deep learning, and the method is based on an unsupervised convolutional neural network structure for monocular depth estimation, andcomprises an encoder, a multi-scale feature fusion module, a gating adaptive decoder, and a refinement unit. The method comprises the following steps: S1, preprocessing a data set; S2, constructing aloss function of the convolutional neural network, inputting a training set image, calculating a loss value of the loss function by using a back propagation algorithm, and performing parameter learning by reducing errors through repeated iteration to enable a prediction value to be close to a real value so as to obtain an optimal weight model of the convolutional neural network; and S3, loading the weight model trained in the step S2, and inputting the test set into an unsupervised convolutional neural network for monocular depth estimation to obtain a depth prediction image. The method solves the problems of large calculation amount during offline training and poor detail part recovery effect in deep reconstruction.

Description

technical field [0001] The invention belongs to the field of image recognition and artificial intelligence, in particular to a monocular depth estimation method based on deep learning. Background technique [0002] In recent years, with the development of computer technology, deep learning has made a series of breakthroughs in the field of computer vision, and using deep learning to obtain the depth of monocular images has also become a popular research field. Depth images contain distance information in the scene, which is the basic task in 3D reconstruction, navigation, target detection and recognition, semantic segmentation, and an important basis for environmental perception and scene understanding. Although the current mainstream uses lidar and depth sensors to obtain object distance information, these sensors are expensive and have certain requirements for the surrounding environment when used. For example, laser attenuation increases sharply in harsh environments such...

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Problems solved by technology

[0004] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a monocular depth estimation method based on deep learning to solve the problems of large amount of calculation and poor recovery of details in depth reconstruction when using deep learning for depth estimation. question

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