A Visual Depth Estimation Method Based on Depth Separable Convolutional Neural Networks

Abstract

The invention discloses a visual depth estimation method based on a depth-separable convolutional neural network. It first constructs a depth-separable convolutional neural network, and its hidden layer includes a convolutional layer, a batch normalization layer, an activation layer, and a maximum pooling layer. , conv_block network block, depth separable convolution network block, Concatanate fusion layer, Add fusion layer, deconvolution layer, separable convolution layer; then use the monocular image in the training set as the original input image, input to the depth separable The convolutional neural network is trained to obtain the estimated depth image corresponding to the monocular image; then by calculating the loss function value between the estimated depth image corresponding to the monocular image in the training set and the corresponding real depth image, the depth can be obtained. The product neural network training model and the optimal weight vector; then input the monocular image to be predicted into the depth separable convolutional neural network training model, and use the optimal weight vector to predict and obtain the corresponding predicted depth image; advantages It has high prediction accuracy.

Description

technical field [0001] The invention relates to a monocular visual depth estimation technology, in particular to a visual depth estimation method based on a depth-separable convolutional neural network. Background technique [0002] In today's rapid development era, with the continuous improvement of the material living standard of the society. Artificial intelligence technology is used in more and more aspects of people's daily life. Computer vision tasks, as one of the representatives of artificial intelligence, have also been paid more and more attention by people. As one of the computer vision tasks, monocular vision depth estimation is becoming more and more important in the technology of assisted driving. [0003] Automobile is one of the indispensable means of transportation for people to travel today, and its development has always been valued by society. Especially with the maturity of artificial intelligence technology, unmanned driving, a representative artifici...

AI Technical Summary

Problems solved by technology

[0005] Laina et al. proposed a depth estimation neural network model based on the fully convolutional residual network. The entire process of the model from the original image input to the predicted depth map output is one-way, although the depth estimation neural network is deep enough and collects Some high-accuracy feature information is obtained, but the share of these high-accuracy feature information in the overall feature information is very small, and due to the singleness of the model, the diversity of features extracted by the model is also insufficient. , during the one-way and long feature collection process, the edge information of the object in the image will be lost, which may lead to a decrease in the overall prediction accuracy

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