Semantic Segmentation Method Based on Efficient Convolutional Networks and Convolutional Conditional Random Fields

Abstract

The invention discloses a semantic segmentation method based on an efficient convolutional network and a convolutional conditional random field. The specific steps of the present invention are as follows: 1. Input an RGB image of any size, and use an encoder network composed of a down-sampling module and a one-dimensional non-bottleneck unit to extract semantics from the original RGB image, and obtain a matrix composed of feature maps; 2. Using a deconvolution layer and a one-dimensional non-bottleneck unit, the discriminative features learned by the encoder network are semantically mapped to the pixel space to obtain dense classification results; 3. Using a convolutional conditional random field network layer, combined with the original The pixel information of the RGB image and the pixel classification information obtained by the decoder network classify the semantic features of the pixels again, so as to achieve the purpose of optimizing the output results. The present invention uses a brand-new encoding and decoding network to classify pixel points end-to-end, and re-optimizes the segmentation result by using a highly efficient convolution conditional random field network.

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 an efficient convolutional network (Efficient ConvNet) and a convolutional conditional random field (Convolutional CRFs) neural network structure. [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 recently popular field of unmanned driving, semantic segmentation technology is used in the extraction of road condition information for unmanned driving; In the medical field, semantic segmentation technology can accurately separate 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) framew...

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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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