An image depth estimation system and method based on hole convolution and semi-supervised learning

Abstract

The invention discloses an image depth estimation system and method based on cavity convolution and semi-supervised learning, and solves the problem of estimating scene depth from a single image. According to the invention, a conventional encoder is adopted; the network structure model of the decoder is improved, and a cavity convolution module is added between an encoder module and a decoder module. The image depth estimation method specifically comprises the steps of obtaining a training set and a test set; using a semi-supervised learning strategy to train a model; testing the precision ofthe model by using a test set; estimating image depth using a model. According to the method, the spatial perception capability of the network is improved by using hole convolution, a semi-supervisedlearning strategy is adopted, and the output depth map is optimized through the depth map smoothing error. The method has the characteristics of small parameter model, high prediction precision and complete detail information. The method is applied to the fields of image three-dimensional reconstruction and automatic driving.

Description

technical field [0001] The present invention belongs to the field of computer vision technology, and mainly relates to image depth estimation, specifically, an image depth estimation system and method based on atrous convolution and semi-supervised learning, which are used for automatic driving, three-dimensional scene modeling, and image understanding tasks, such as Semantic segmentation, object tracking, etc. Background technique [0002] Image depth estimation is one of the important topics of computer vision. It can effectively help the computer understand the three-dimensional structural relationship of the scene, thus providing the possibility for more advanced image processing. Image depth estimation plays a very important role in various applications in the field of computer vision. For example, robot navigation and car driverless driving need to rely on the depth information of the scene to accurately locate themselves in real time and judge surrounding obstacles; ...

AI Technical Summary

Problems solved by technology

But there is no effective true depth as a constraint, and the method is also limited in prediction accuracy

[0006] At the same time, the existing image depth estimation methods based on deep learning ignore the dependence of the depth estimation task on spatial information.

When using a deep convolutional network, a large number of pooling operations reduces the spatial resolution of the features, which leads to the loss of spatial information and has a non-negligible impact on the output results.

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