An image semantic segmentation method based on a multi-layer information fusion full convolutional neural network

A convolutional neural network and semantic segmentation technology, applied in the field of image semantic segmentation based on multi-layer information fusion full convolutional neural network, can solve the problem of low image segmentation accuracy, and achieve low accuracy and good image semantic segmentation. the effect of the result

Inactive Publication Date: 2019-06-18
CHINA JILIANG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to design an image semantic segmentation method based on multi-layer info

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  • An image semantic segmentation method based on a multi-layer information fusion full convolutional neural network
  • An image semantic segmentation method based on a multi-layer information fusion full convolutional neural network
  • An image semantic segmentation method based on a multi-layer information fusion full convolutional neural network

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[0033] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0034] like figure 1 As shown, an image semantic segmentation method based on multi-layer information fusion global convolutional neural network includes the following steps:

[0035] S1. Construct a fully convolutional neural network based on multi-layer information fusion;

[0036] like figure 2 As shown, in the above step S1, the deep neural network includes a sequentially connected feature extraction module and feature fusion module;

[003...

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Abstract

The invention relates to an image semantic segmentation method based on a multi-layer information fusion full convolutional neural network. The method comprises the following steps: constructing the multi-information fusion full convolutional neural network; Preprocessing the training picture of the data set, and inputting a preprocessing result into a neural network for training to obtain an image semantic segmentation model; Inputting the to-be-tested image into the image semantic segmentation model, performing one-time forward propagation, and outputting a semantic segmentation result in anend-to-end manner. Basic depth features of the image are extracted by using a convolutional neural network model; According to the method, the features are divided into the low-level features and thehigh-level features, and the low-level features and the high-level features are fused into the enhanced depth features, so that the problems of low image semantic segmentation accuracy and image spatial detail information loss can be effectively solved, and finally, a better image semantic segmentation result is obtained.

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 multi-layer information fusion full convolutional 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] Although image semantic segmentation methods have been developed rapidly, there are still many problems to be solved because of its complexity. The challenges of image seman...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
Inventor 陈亮华静李子印刘泽森孟庆阳姚冰峰孔繁圣
Owner CHINA JILIANG UNIV
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