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 provide

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