Semantic segmentation method based on efficient convolutional network and convolutional conditional random field

Abstract

The invention discloses a semantic segmentation method based on an efficient convolutional network and a convolutional conditional random field. The method comprises the following specific steps of: 1, inputting an RGB image with any size, and performing semantic extraction on the original RGB image by adopting an encoder network consisting of a down-sampling module and a one-dimensional non-bottleneck unit to obtain a matrix consisting of characteristic patterns; 2, adopting a deconvolution layer and a one-dimensional non-bottleneck unit to semantically map the discriminative features learned by the encoder network to a pixel space to obtain a dense classification result; and 3, adopting the convolutional conditional random field network layer and pixel point information of the original RGB image and pixel point classification information obtained by the decoder network to classify pixel point semantic features again, so that the purpose of output result optimization is achieved. A brand new coding and decoding network is adopted to classify the end-to-end pixel points, and a segmentation result is re-optimized through a convolutional conditional random field network with high use efficiency.

Description

Technical field [0001] The invention belongs to image object detection and object segmentation in the field of computer vision and artificial intelligence. Specifically, it relates to a semantic segmentation method based on the neural network structure of Efficient ConvNet and Convolutional CRFs. [0002] technical background [0003] Semantic segmentation is an important part of image understanding in computer vision. It has a wide range of applications in the real world. For example, in the field of unmanned driving, which is very popular recently, semantic segmentation technology is used in unmanned road condition information extraction; In the medical field, semantic segmentation technology can accurately separate the various organs of the human body. [0004] In recent years, semantic segmentation technology has become more and more mature. In 2015, the new Fully Convolutional Networks (FCN) framework proposed by Jonathan Long et al. allowed the rapid development of semantic se...

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Problems solved by technology

[0005] The purpose of the present invention is to improve the problem that most current semantic segmentation methods need to spend expensive calculation costs to ensure high accuracy,

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