An image semantic segmentation method based on depth neural network

Abstract

The invention discloses an image semantic segmentation method based on a depth neural network, comprising the following steps: S1, constructing a depth neural network based on a dense connection network and a pooling of a porous space pyramid; S2, pre-processing the training picture of the data set, and inputting the pre-processing result into the depth neural network for training to obtain the image semantic segmentation model; S3, inputting the image to be tested into the image semantic segmentation model, performing forward propagation, and outputting the semantic segmentation result end-to-end. The densely connected network does not desample the image, but keeps the spatial dimension of the image for training, and the multi-scale information of the image is captured by the multi-aperture spatial pyramid pooling module, which can effectively solve the problem of low accuracy of image semantic segmentation and loss of spatial detail information of the image, and finally obtain a better image semantic segmentation result.

Description

technical field [0001] The invention belongs to the technical field of image semantic segmentation, and in particular relates to an image semantic segmentation method based on a deep neural network. Background technique [0002] Deep convolutional neural networks have pushed the performance of computer vision systems to new heights. Image semantic segmentation is one of the important tasks of computer vision, and it plays a vital role in subsequent computer vision tasks, such as the distinction between road and non-road scenes in unmanned driving video analysis. Image semantic segmentation is generally modeled as a pixel-level multi-classification problem, and its goal is to distinguish each pixel of an image into one of several predefined categories. [0003] Traditional image semantic segmentation methods generally extract artificially designed features from small windows in the neighborhood of image pixels for discrimination, such as texture features. At the same time, ...

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Problems solved by technology

[0006] In view of the above-mentioned deficiencies in the prior art, the image semantic feature extraction method based on the deep neural network provided by the present invention solves the problems of low accuracy of image semantic segmentation and loss of image spatial detail information in the prior art

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